Welding detection method in automobile machining process and related system

Abstract

The invention provides a welding detection method and a related system in an automobile machining process, and the method comprises the steps: carrying out the first feature extraction according to a first welding region image, obtaining a first feature set, and carrying out the matching of the first feature set and a first reference feature set, and obtaining a first matching value; determining a first welding parameter and a first welding detection standard parameter corresponding to the first welding position; performing multi-scale decomposition on the first welding area image according to a first multi-scale decomposition algorithm and the first algorithm control parameter to obtain a low-frequency component part and m high-frequency component parts; performing feature extraction on the m high-frequency component parts to obtain k high-frequency feature parameters; matching the k high-frequency characteristic parameters with k standard high-frequency characteristic parameters to obtain a second matching value; and determining a target matching value according to the first matching value and the second matching value. According to the invention, the welding detection efficiency in the automobile processing process can be improved.

Description

Technical Field

[0001] The present application relates to the field of automotive technology, and in particular to a welding detection method and a related system during automobile processing. Background Art

[0002] In the automotive processing industry, the quality of welding directly affects the performance of the vehicle. In practical applications, after welding is completed, the welding inspection process is also very important. At present, the welding inspection efficiency is low. Therefore, how to improve the welding inspection efficiency in the automotive processing process needs to be solved urgently. Summary of the invention

[0003] The embodiments of the present application provide a welding detection method and a related system during automobile processing, which can improve the welding detection efficiency during automobile processing.

[0004] In a first aspect, an embodiment of the present application provides a welding detection method in an automobile processing process, which is applied to an industrial robot, wherein the industrial robot includes a visual system and a welding system, and the method includes: Acquire a first image for a first welding position by the visual system, where the first welding position is a welded position between a first automobile component and a second automobile component; Performing region segmentation on the first image to obtain a first welding region image, performing first feature extraction based on the first welding region image to obtain a first feature set, acquiring a first reference feature set corresponding to the first welding position, and matching the first feature set with the first reference feature set to obtain a first matching value; When the first matching value is within a first preset range, the first welding parameter and the first welding detection standard parameter corresponding to the first welding position are determined by the welding system, the first welding detection standard parameter includes k standard high-frequency characteristic parameters, each standard high-frequency characteristic parameter corresponds to at least one high-frequency characteristic type of at least one high-frequency component part; k is a positive integer; the first preset range includes a first upper threshold and a first lower threshold; determining a first multi-scale decomposition algorithm corresponding to the first welding parameter and a first algorithm control parameter of the first multi-scale decomposition algorithm corresponding to the first welding detection standard parameter; The first welding area image is multi-scale decomposed according to the first multi-scale decomposition algorithm and the first algorithm control parameter to obtain a low-frequency component part and m high-frequency component parts; m is a positive integer; Extracting features of the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameter to obtain k kinds of high-frequency feature parameters; Matching the k kinds of high-frequency characteristic parameters with the k kinds of standard high-frequency characteristic parameters to obtain a second matching value; Determine a target matching value according to the first matching value and the second matching value; When the target matching value is greater than or equal to the first upper limit threshold, it is determined that the welding inspection of the first welding position is qualified.

[0005] In a second aspect, an embodiment of the present application provides a welding detection system in an automobile processing process, which is applied to an industrial robot, wherein the industrial robot includes a visual system and a welding system, and the welding detection system in the automobile processing process includes: An acquisition unit, configured to acquire a first image for a first welding position through the visual system, where the first welding position is a welded position between a first automobile component and a second automobile component; a segmentation unit, configured to perform region segmentation on the first image to obtain a first welding region image, perform first feature extraction based on the first welding region image to obtain a first feature set, obtain a first reference feature set corresponding to the first welding position, and match the first feature set with the first reference feature set to obtain a first matching value; a determination unit, configured to determine, by the welding system, a first welding parameter and a first welding detection standard parameter corresponding to the first welding position when the first matching value is within a first preset range, the first welding detection standard parameter including k standard high-frequency feature parameters, each standard high-frequency feature parameter corresponding to at least one high-frequency feature type of at least one high-frequency component part; k is a positive integer; the first preset range includes a first upper threshold and a first lower threshold; determine a first multi-scale decomposition algorithm corresponding to the first welding parameter and a first algorithm control parameter of the first multi-scale decomposition algorithm corresponding to the first welding detection standard parameter; a decomposition unit, configured to perform multi-scale decomposition on the first welding region image according to the first multi-scale decomposition algorithm and the first algorithm control parameter to obtain a low-frequency component part and m high-frequency component parts; m is a positive integer; an extraction unit, configured to extract features of the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameter, so as to obtain k kinds of high-frequency feature parameters; A matching unit, configured to match the k kinds of high-frequency characteristic parameters with the k kinds of standard high-frequency characteristic parameters to obtain a second matching value; The determination unit is used to determine a target matching value according to the first matching value and the second matching value; when the target matching value is greater than or equal to the first upper limit threshold, determine that the welding detection of the first welding position is qualified.

[0006] In a third aspect, an embodiment of the present application provides an industrial robot, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.

[0007] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the embodiment of the present application.

[0008] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0009] The implementation of the embodiments of the present application has the following beneficial effects: It can be seen that the welding detection method and related system in the automobile processing process described in the embodiment of the present application are applied to an industrial robot, which includes a visual system and a welding system. The first image for the first welding position is obtained by the visual system, and the first welding position is a welded position between the first automobile part and the second automobile part; the first image is segmented to obtain a first welding area image, a first feature is extracted according to the first welding area image to obtain a first feature set, a first reference feature set corresponding to the first welding position is obtained, and the first feature set is matched with the first reference feature set to obtain a first matching value; when the first matching value is within a first preset range, the first welding parameter and the first welding detection standard parameter corresponding to the first welding position are determined by the welding system, and the first welding detection standard parameter includes k standard high-frequency feature parameters, each of which is k standard high-frequency feature parameters. A standard high-frequency feature parameter corresponds to at least one high-frequency feature type of at least one high-frequency component part; k is a positive integer; the first preset range includes a first upper threshold value and a first lower threshold value; determine a first multi-scale decomposition algorithm corresponding to a first welding parameter and a first algorithm control parameter of the first multi-scale decomposition algorithm corresponding to a first welding detection standard parameter; perform multi-scale decomposition on the first welding area image according to the first multi-scale decomposition algorithm and the first algorithm control parameter to obtain a low-frequency component part and m high-frequency component parts; m is a positive integer; perform feature extraction on the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameter to obtain k high-frequency feature parameters; match the k high-frequency feature parameters with the k standard high-frequency feature parameters to obtain a second matching value; determine a target matching value according to the first matching value and the second matching value;When the target matching value is greater than or equal to the first upper limit threshold, it is determined that the welding inspection of the first welding position is qualified. Firstly, the characteristics of the qualified weld corresponding to the welding position can be obtained, and the first feature set is matched with the first reference feature set to obtain the first matching value, so as to preliminarily realize welding inspection from the appearance level. Secondly, when the first matching value is in the first preset range, it means that the welding appearance is general, and the first welding parameter and the first welding inspection standard parameter corresponding to the first welding position can be determined by the welding system. In this way, the welding parameters corresponding to the welding position and the corresponding welding inspection standard parameters can be obtained. Thirdly, not only the multi-scale decomposition algorithm corresponding to the welding process can be adopted, so as to ensure the deep extraction of the welding features corresponding to the welding process, but also the corresponding algorithm control is performed based on the welding inspection standard, which is helpful to deeply extract the welding features corresponding to the welding process, thereby helping to improve automobile processing. The welding detection efficiency in the process is improved. Fourthly, the low-frequency component part reflects the image subject, and the m high-frequency component parts reflect the image details. The low-frequency component part and the m high-frequency component parts reflect the degree of influence of the image subject on the image details, and the first welding detection standard parameter reflects the welding detection standard corresponding to the welding process, that is, the corresponding high-frequency feature parameters can be extracted based on the degree of influence of the image subject on the image details (the characteristics of the image itself) and the welding detection standard corresponding to the welding process, so that the welding features corresponding to the welding process can be deeply extracted. Fifthly, the welding quality is evaluated based on the welding features at the appearance level and the depth of the deep welding details, that is, the target matching value is determined according to the first matching value and the second matching value. When the target matching value is greater than or equal to the first upper limit threshold, it means that the welding is good, that is, it is determined that the welding detection of the first welding position is qualified, thereby helping to improve the welding detection efficiency in the automobile processing process. ; BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0011] Figure 1 It is a flow chart of a welding detection method in an automobile processing process provided by an embodiment of the present application; Figure 2 is a schematic diagram of the structure of an industrial robot provided in an embodiment of the present application; Figure 3 This is a block diagram of the functional units of a welding detection system in an automobile processing process provided by an embodiment of the present application. DETAILED DESCRIPTION

[0012] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but in a possible example also includes steps or units that are not listed, or in a possible example also includes other steps or units inherent to these processes, methods, products or devices.

[0013] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0014] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0015] In the embodiment of the present application, the industrial robot may include various industrial robots with visual inspection functions in the automobile processing process, such as automobile processing robots, intelligent machine tools, humanoid robots, etc., which are not limited here.

[0016] See also Figure 1 , Figure 1 1 is a flow chart of a welding detection method in an automobile processing process provided by an embodiment of the present application, which is applied to an industrial robot. The industrial robot includes a visual system and a welding system. The welding detection method in the automobile processing process includes: 101. Acquire a first image for a first welding position through the visual system, where the first welding position is a welded position between a first automobile component and a second automobile component.

[0017] The first automobile part and the second automobile part can be automobile parts of the same or different types. The first automobile part and the second automobile part can be understood as automobile parts that need to be welded, and can include at least one of the following: frame body, battery busbar, screws, doors, chassis, axles, nuts, etc., without limitation here. The visual system may include at least one visual sensor, and the visual sensor may include at least one of the following: a camera, an ultrasonic sensor, a radar sensor, an infrared sensor, etc., which are not limited here.

[0018] The welding system may include a welding gun, which may record welding parameters of each welding position during the welding process and realize the welding function.

[0019] In a specific implementation, a first image for a first welding position may be acquired through a visual system, where the first welding position is any welding position among welded positions between the first automobile component and the second automobile component.

[0020] 102. Perform region segmentation on the first image to obtain a first welding region image, perform first feature extraction based on the first welding region image to obtain a first feature set, obtain a first reference feature set corresponding to the first welding position, match the first feature set with the first reference feature set to obtain a first matching value.

[0021] Among them, the first feature set may include one or more features, which may include at least one of the following: solder joint color, average solder joint width, maximum solder joint width, minimum solder joint width, average solder joint thickness, maximum solder joint thickness, minimum solder joint thickness, etc., which are not limited here.

[0022] Among them, the first reference feature set may include one or more features, which may include at least one of the following: weld color, average weld width, maximum weld width, minimum weld width, average weld thickness, maximum weld thickness, minimum weld thickness, etc., which are not limited here.

[0023] In a specific implementation, the first image can be segmented to obtain a first welding area image. The first welding area image can be understood as an image corresponding to the welding area only. In a specific implementation, image recognition can be performed on the first welding area image to extract a first feature and obtain a first feature set.

[0024] In a specific implementation, different welding positions correspond to different welding processes, and different welding processes correspond to different qualified welding features. The mapping relationship between the preset welding position and the reference feature set can be pre-stored, and then the first reference feature set corresponding to the first welding position can be determined based on the mapping relationship. In this way, the features of the qualified welds corresponding to the welding positions can be obtained, and then the first feature set can be matched with the first reference feature set to obtain a first matching value. In this way, welding detection can be preliminarily realized from the appearance level, which can improve the welding detection efficiency during automobile processing.

[0025] 103. When the first matching value is within a first preset range, the first welding parameter and the first welding detection standard parameter corresponding to the first welding position are determined by the welding system, the first welding detection standard parameter includes k standard high-frequency characteristic parameters, each standard high-frequency characteristic parameter corresponds to at least one high-frequency characteristic type of at least one high-frequency component part; k is a positive integer; the first preset range includes a first upper limit threshold and a first lower limit threshold.

[0026] The first preset range may be preset or set by system default. The first preset range includes a first upper threshold and a first lower threshold, and the first lower threshold is smaller than the first upper threshold.

[0027] Among them, the welding system includes: wire feeder, welding power supply and welding gun.

[0028] Among them, the first welding parameters corresponding to the first welding position may include at least one of the following: welding position coordinates, welding temperature, welding trajectory, welding rate of the welding gun, wire feeding rate of the wire feeder, power supply parameters of the welding power supply, welding action parameters, etc., which are not limited here.

[0029] Among them, the power supply parameters of the welding power supply may include at least one of the following: the working current of the welding power supply, the working voltage of the welding power supply, the working power of the welding power supply, the working mode of the welding power supply, etc., which are not limited here.

[0030] Among them, the welding action parameters may include at least one of the following: welding action amplitude, welding process, welding action trajectory, welding angle, etc., which are not limited here.

[0031] Among them, the standard high-frequency characteristic parameters can be understood as characteristic parameters of the high-frequency component part, and the standard high-frequency characteristic parameters can include at least one of the following: characteristic points, characteristic values, characteristic patterns, characteristic vectors, the total number of characteristic points, the distribution density of characteristic points (the ratio between the total number of characteristic points and the area of ​​the characteristic region), etc., which are not limited here. In a specific implementation, the first welding detection standard parameter can include k standard high-frequency characteristic parameters, each of which corresponds to at least one high-frequency characteristic type of at least one high-frequency component part; k is a positive integer.

[0032] In a specific implementation, different welding positions may correspond to different welding detection standard parameters, that is, the mapping relationship between preset welding positions and welding detection standard parameters may be pre-stored, and then, the first welding detection standard parameters corresponding to the first welding position may be determined based on the mapping relationship. In this way, the welding detection standard parameters corresponding to the welding process may be obtained.

[0033] Alternatively, different welding positions may also correspond to different welding detection standard parameter sets, that is, the mapping relationship between the preset welding positions and the welding detection standard parameter sets may be pre-stored, and then, the first welding detection standard parameter set corresponding to the first welding position may be determined based on the mapping relationship. The first welding detection standard parameter set may include multiple welding detection standard parameters, each welding detection standard parameter may correspond to a welding parameter, and then, the first welding detection standard parameter corresponding to the first welding parameter may be selected from the first welding detection standard parameter set. In this way, the welding detection standard parameters corresponding to the welding position and the welding process may be obtained.

[0034] The first welding inspection standard parameter can be understood as the welding inspection standard parameter corresponding to the first welding position under qualified conditions. The first welding inspection standard parameter can guide to a certain extent which type of high-frequency features need to be extracted, or which layer of high-frequency features, or perform corresponding processing on one or more high-frequency features in one or more layers of high-frequency component parts, for example, taking the average, counting the number, summing, etc.

[0035] In a specific implementation, when the first matching value is within a first preset range, that is, the first matching value is greater than or equal to a first lower threshold value and less than or equal to a first upper threshold value, it indicates that the welding appearance is average. Then, the first welding parameter and the first welding detection standard parameter corresponding to the first welding position can be determined by the welding system. In this way, the corresponding welding parameters of the welding position and the corresponding welding detection standard parameters can be obtained. In this way, the welding detection efficiency in the automobile processing process can be improved.

[0036] 104. Determine a first multi-scale decomposition algorithm corresponding to the first welding parameter and a first algorithm control parameter of the first multi-scale decomposition algorithm corresponding to the first welding detection standard parameter.

[0037] In a specific implementation, the multi-scale decomposition algorithm may include at least one of the following: a wavelet transform algorithm, a contourlet transform algorithm, a non-subsampled contourlet transform algorithm, a Laplace pyramid transform algorithm, etc., which are not limited here. Specifically, a mapping relationship between preset welding parameters and the multi-scale decomposition algorithm can be pre-stored, and then, based on the mapping relationship, a first multi-scale decomposition algorithm corresponding to the first welding parameter is determined, that is, different welding parameters reflect different welding processes, and different welding processes require different welding features to be detected. In this way, a multi-scale decomposition algorithm corresponding to the welding process can be adopted, thereby ensuring deep extraction of welding features corresponding to the welding process.

[0038] In a specific implementation, a plurality of algorithm control parameters of a preset first multiscale decomposition algorithm may also be pre-stored, each algorithm control parameter corresponding to a welding detection standard parameter, and then, a first algorithm control parameter of the first multiscale decomposition algorithm corresponding to the first welding detection standard parameter may be determined, wherein the first algorithm control parameter is used to control the algorithm effect of the first multiscale decomposition algorithm, and the algorithm effect may include at least one of the following: algorithm speed, the number of layers of the high-frequency component part, the degree of multiscale decomposition, etc., which are not limited here, that is, corresponding algorithm control is performed based on the welding detection standard, which helps to deeply extract welding features corresponding to the welding process, thereby helping to improve the welding detection efficiency in the automobile processing process.

[0039] 105. Perform multi-scale decomposition on the first welding area image according to the first multi-scale decomposition algorithm and the first algorithm control parameter to obtain a low-frequency component part and m high-frequency component parts; m is a positive integer.

[0040] In a specific implementation, the first welding area image can be multi-scale decomposed according to the first multi-scale decomposition algorithm and the first algorithm control parameters to obtain a low-frequency component part and m high-frequency component parts; m is a positive integer, and not only the multi-scale decomposition algorithm corresponding to the welding process can be adopted, so as to ensure the deep extraction of welding features corresponding to the welding process, but also the corresponding algorithm control is performed based on the welding detection standard, which is helpful for the deep extraction of welding features corresponding to the welding process, thereby helping to improve the welding detection efficiency in the automobile processing process.

[0041] 106. Perform feature extraction on the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameters to obtain k high-frequency feature parameters.

[0042] Among them, the low-frequency component part reflects the image body, the m high-frequency component parts reflect the image details, and the low-frequency component part and the m high-frequency component parts reflect the influence of the image body on the image details. In the specific implementation, the m high-frequency component parts are feature extracted according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameters to obtain k high-frequency feature parameters. Since the first welding detection standard parameters reflect the welding detection standard corresponding to the welding process, the corresponding high-frequency feature parameters can be extracted based on the influence of the image body on the image details (the characteristics of the image itself) and the welding detection standard corresponding to the welding process. Therefore, the welding features corresponding to the welding process can be deeply extracted, which is helpful to improve the welding detection efficiency in the automobile processing process.

[0043] Optionally, the above step 106, extracting features of the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameter to obtain k high-frequency feature parameters, may include the following steps: Determining a first information entropy of the low-frequency component part; determining a second information entropy of the first welding area image; Determine a ratio between the first information entropy and the second information entropy to obtain a first ratio; When the first ratio is within a second preset range, the m high-frequency component parts are feature extracted according to the first welding detection standard parameters to obtain the k high-frequency feature parameters; the second preset range includes a second upper threshold and a second lower threshold.

[0044] The second preset range can be preset or set by system default, and the second preset range includes a second upper threshold and a second lower threshold. The second lower threshold is smaller than the second upper threshold.

[0045] In a specific implementation, the first information entropy of the low-frequency component part can be determined, the second information entropy of the first welding area image can be determined, and the ratio between the first information entropy and the second information entropy can be determined to obtain a first ratio, the first ratio=first information entropy / second information entropy, the first ratio reflects the degree of influence of the image subject on the image details, when the first ratio is in the second preset range, that is, the first ratio is greater than or equal to the first lower limit threshold and less than or equal to the first upper limit threshold, it means that the relationship between the image subject and the image details meets the requirements, because the first welding detection standard parameter can guide to a certain extent which type of high-frequency feature needs to be extracted, or which layer of high-frequency features, or one or more high-frequency features in one or more layers of high-frequency component parts are processed accordingly, therefore, according to the first welding detection standard parameter, feature extraction can be performed on m high-frequency component parts to obtain k high-frequency feature parameters, so that the high-frequency feature parameters corresponding to the first welding detection standard parameter can be extracted, and then, the subsequent matching accuracy can be guaranteed, thereby helping to improve the welding detection efficiency in the automobile processing process.

[0046] Optionally, the following steps may also be included: When the first ratio is greater than the second upper limit threshold, determining a third relative deviation between the first ratio and the second upper limit threshold, determining a first high-frequency image enhancement algorithm corresponding to the third relative deviation, determining a first mean between the first matching value and the second matching value; and determining a first control parameter corresponding to the first high-frequency image enhancement algorithm corresponding to the first mean; Performing image enhancement on the m high-frequency component parts according to the first high-frequency image enhancement algorithm and the first control parameter; The m high-frequency component parts after image enhancement are subjected to feature extraction according to the first welding detection standard parameters to obtain the k kinds of high-frequency feature parameters.

[0047] In a specific implementation, when the first ratio is greater than the second upper limit threshold, it means that the main body of the image obscures the image details. Then, a third relative deviation between the first ratio and the second upper limit threshold can be determined, that is, the third relative deviation = (first ratio - second upper limit threshold) / (first ratio + second upper limit threshold), that is, the third relative deviation reflects the degree to which the main body of the image obscures the image details. The mapping relationship between the preset relative deviation and the high-frequency image enhancement algorithm can be pre-stored. The high-frequency image enhancement algorithm is used to implement high-frequency image enhancement, thereby improving the significance of image details and ensuring the integrity of welding feature extraction and the quality of welding features. Then, the first high-frequency image enhancement algorithm corresponding to the third relative deviation can be determined based on the mapping relationship. In addition, the first matching value and the second matching value can also be mapped. A mean operation is performed, that is, the first mean = (first matching value + second matching value) / 2. The first mean roughly reflects the welding quality. Furthermore, the mapping relationship between the preset mean and the control parameter corresponding to the first high-frequency image enhancement algorithm can be pre-stored. Furthermore, the first control parameter corresponding to the first mean can be determined based on the mapping relationship. The first control parameter is used to control the algorithm effect of the first high-frequency image enhancement algorithm. The algorithm effect may include at least one of the following: high-frequency image enhancement degree, high-frequency image enhancement speed, high-frequency image enhancement area, which layer of high-frequency component part requires high-frequency image enhancement, which type of characteristics requires high-frequency image enhancement, etc., which are not limited here. In this way, the algorithm effect of the first high-frequency image enhancement algorithm can be dynamically adjusted based on the welding quality, so that the depth of significance enhancement of image details is in line with the actual situation.

[0048] Next, the m high-frequency component parts are image enhanced according to the first high-frequency image enhancement algorithm and the first control parameter, thereby improving the significance of image details and ensuring the integrity of welding feature extraction and welding feature quality. Since the first welding detection standard parameter can guide to a certain extent which type of high-frequency feature needs to be extracted, or which layer of high-frequency features, or one or more high-frequency features in one or more layers of high-frequency component parts are processed accordingly, the high-frequency component part after image enhancement can be feature extracted according to the first welding detection standard parameter to obtain k high-frequency feature parameters. In this way, the high-frequency feature parameters corresponding to the first welding detection standard parameter can be extracted, and then the subsequent matching accuracy can be guaranteed, thereby helping to improve the welding detection efficiency in the automobile processing process.

[0049] Optionally, the following steps may also be included: When the first ratio is less than the second lower threshold, determining a fourth relative deviation between the second upper threshold and the first ratio, determining a first low-frequency image enhancement algorithm corresponding to the fourth relative deviation, determining a first difference between the first matching value and the first lower threshold, and determining a second control parameter of the first low-frequency image enhancement algorithm corresponding to the first difference; Performing image enhancement on the low-frequency component part according to the first low-frequency image enhancement algorithm and the second control parameter; Reconstructing the image based on the low-frequency component part and the m high-frequency component parts after image enhancement to obtain a second welding area image; Performing multi-scale decomposition on the second welding area image according to the first multi-scale decomposition algorithm and the first algorithm control parameter to obtain m target high-frequency component parts; The m target high-frequency component parts are subjected to feature extraction according to the first welding detection standard parameters to obtain the k types of high-frequency feature parameters.

[0050] In a specific implementation, when the first ratio is less than the second lower threshold, it means that the main body of the image is insufficient, but the details are too significant, and the fourth relative deviation between the second upper threshold and the first ratio can be determined, the fourth relative deviation = (second upper threshold-first ratio) / (second upper threshold+first ratio), the fourth relative deviation reflects the degree of significance of the image details. The mapping relationship between the preset relative deviation and the low-frequency image enhancement algorithm can be pre-stored. The low-frequency image enhancement algorithm is used to achieve low-frequency image enhancement, thereby improving the saturation of the main body of the image to suppress the significance of the image details and ensure the accuracy of welding feature extraction. Then, the first low-frequency image enhancement algorithm corresponding to the fourth relative deviation can be determined based on the mapping relationship, and then the first matching can be determined. The first difference between the matching value and the first lower threshold, the first difference = the first matching value - the first lower threshold, the appearance level is related to the depth of the image body, especially in terms of appearance, in the case of general welding, and then, based on the mapping relationship between the difference and the control parameter of the first low-frequency image enhancement algorithm, the second control parameter of the first low-frequency image enhancement algorithm corresponding to the first difference can be determined based on the mapping relationship, and the second control parameter is used to control the algorithm effect of the first low-frequency image enhancement algorithm, and the algorithm effect may include at least one of the following: low-frequency image enhancement degree, low-frequency image enhancement speed, low-frequency image enhancement area, etc., which are not limited here. In this way, the algorithm effect of the first low-frequency image enhancement algorithm can be dynamically adjusted based on the appearance of the welding, so that the saturation enhancement depth of the image body is in line with the actual situation.

[0051] Furthermore, the low-frequency component part can be enhanced according to the first low-frequency image enhancement algorithm and the second control parameter, so that the saturation enhancement depth of the image body conforms to the actual situation, and then the low-frequency component part and the m high-frequency component parts after the image enhancement are reconstructed to obtain the second welding area image, and the second welding area image is multi-scale decomposed according to the first multi-scale decomposition algorithm and the first algorithm control parameter to obtain m target high-frequency component parts, and then the feature extraction of the m target high-frequency component parts according to the first welding detection standard parameter is performed to obtain the k kinds of high-frequency feature parameters. Since the image details are too significant, the image details need to be reconstructed after refilling. , reconstruct and then extract the high-frequency component part, thereby, the accuracy of welding feature extraction can be guaranteed. In addition, since the first welding detection standard parameter can guide to a certain extent which type of high-frequency feature needs to be extracted, or which layer of high-frequency features, or one or more high-frequency features in one or more layers of high-frequency component parts are processed accordingly, therefore, m high-frequency component parts can be feature extracted according to the first welding detection standard parameter to obtain k high-frequency feature parameters. In this way, the high-frequency feature parameters corresponding to the first welding detection standard parameter can be extracted, and then the subsequent matching accuracy can be guaranteed, thereby helping to improve the welding detection efficiency in the automobile processing process.

[0052] 107. Match the k kinds of high-frequency characteristic parameters with the k kinds of standard high-frequency characteristic parameters to obtain a second matching value.

[0053] In a specific implementation, each of the k high-frequency feature parameters can be matched with the corresponding standard high-frequency feature parameter in the k standard high-frequency feature parameters to obtain k matching values. The k matching values ​​can be weighted to obtain a second matching value, that is, each of the k standard high-frequency feature parameters corresponds to a weight, so that k weights can be obtained, and the sum of the k weights is 1. The k weights can be preset or the system defaults. Since the welding features corresponding to the welding process are deeply extracted and the corresponding welding features are matched with the standard welding features, high-frequency feature verification can be achieved through the depth of image details to obtain welding detection results, which helps to improve the welding detection efficiency in the automobile processing process.

[0054] 108. Determine a target matching value according to the first matching value and the second matching value.

[0055] In the embodiment of the present application, the welding quality can be evaluated based on the welding features at the appearance level and the depth of the deep welding details, that is, the target matching value is determined according to the first matching value and the second matching value, which helps to improve the welding detection efficiency during the automobile processing process.

[0056] Optionally, the first welding detection standard parameter also includes a standard matching value; the above step 108, determining a target matching value according to the first matching value and the second matching value, may include the following steps: Determining a first relative deviation between the first upper threshold and the first matching value; When the first relative deviation is less than or equal to a preset relative deviation, determining a first deviation between the second matching value and the standard matching value; determining a first adjustment parameter corresponding to the first deviation; Determining the target matching value according to the first adjustment parameter and the first matching value; When the first relative deviation is greater than the preset relative deviation, determining a second relative deviation between the first matching value and the first lower threshold; Determine a target weight pair corresponding to the second relative deviation, the target weight pair comprising a first weight and a second weight, and a sum of the first weight and the second weight is 1; A weighted operation is performed according to the first matching value, the second matching value and the target weight to obtain the target matching value.

[0057] Among them, the first welding detection standard parameter can also include a standard matching value, which can be understood as a matching threshold. When the second matching value is greater than the standard matching value, it means that the welding is good from the perspective of the detailed features. Conversely, when the second matching value is less than or equal to the standard matching value, it means that the welding is average from the perspective of the detailed features.

[0058] The preset relative deviation may be preset or set by system default.

[0059] In a specific implementation, the first relative deviation between the first upper threshold and the first matching value can be determined, the first relative deviation = (first upper threshold - first matching value) / (first upper threshold + first matching value), the first relative deviation includes not only the relative deviation band, but also the relative deviation direction. When the first relative deviation is less than or equal to the preset relative deviation, it means that the first matching value is very close to the first upper threshold. From the appearance, the welding is close to good, but there are slight flaws. Then, the welding verification can be further carried out in depth in combination with the welding details, that is, the first deviation between the second matching value and the standard matching value can be determined, the first deviation = (second matching value - standard matching value) / standard matching value. The first deviation includes not only the deviation size, but also the deviation direction. The first deviation reflects the quality of the welding details.

[0060] Next, a mapping relationship between a preset deviation and an adjustment parameter can be pre-stored, wherein the value range of the adjustment parameter can be pre-set or system default, for example, the value range of the adjustment parameter is -0.2~0.2, and then, based on the mapping relationship, a first adjustment parameter corresponding to the first deviation can be determined, and then the target matching value is determined according to the first adjustment parameter and the first matching value, that is, the target matching value = (1+first adjustment parameter)*first matching value. Since the welding is close to good in appearance, it is considered that the matching result at the appearance level (first matching value) is completely credible, but there are slight flaws, which means that the recognition of the welding details can deeply influence the welding verification result. Then, the degree of deviation between the actual welding details and the standard matching value can be used to dynamically adjust the credible matching result (first matching value), so that the final matching value depth conforms to the actual situation, which helps to improve the welding detection efficiency during automobile processing.

[0061] Correspondingly, when the first relative deviation is greater than the preset relative deviation, the welding deviation is good in appearance, that is, the welding is general, and it is considered that the matching result (first matching value) at the appearance level is not completely credible, but the credibility (weight) is between 0 and 1, and then the second relative deviation between the first matching value and the first lower limit threshold is determined. The second relative deviation not only includes the relative deviation band, but also the relative deviation direction. The second relative deviation = (first matching value-first lower limit threshold) / (first matching value+first lower limit threshold), and the mapping relationship between the preset relative deviation and the weight pair can also be pre-stored. The weight pair includes 2 weights, one weight corresponding to the first matching value, and the other weight corresponding to the second matching value. Then, based on the mapping relationship, the target weight pair corresponding to the second relative deviation can be determined. The target weight pair includes the first weight and the second weight, and the sum of the first weight and the second weight is 1. Among them, the larger the second relative deviation, the larger the first weight, and the smaller the second relative deviation, the smaller the first weight.

[0062] Next, a weighted operation is performed based on the first matching value, the second matching value and the target weight pair to obtain the target matching value, that is, the target matching value = the first weight * the first matching value + the second weight * the second matching value. In this way, in terms of appearance, the welding deviation is good, that is, in the case of general welding, the corresponding weight pair is dynamically determined based on the degree of deviation from the first lower limit threshold, that is, in the case of general welding, the detail features are used to compensate for the appearance of insufficient welding, so that the final welding detection result depth is consistent with the actual situation, which helps to improve the welding detection efficiency in the automobile processing process.

[0063] 109. When the target matching value is greater than or equal to the first upper limit threshold, determine that the welding inspection of the first welding position is qualified.

[0064] In a specific implementation, when the target matching value is greater than or equal to the first upper limit threshold, it indicates that the welding is good, that is, it is determined that the welding inspection of the first welding position is qualified, thereby helping to improve the welding inspection efficiency in the automobile processing process.

[0065] Optionally, the following steps may also be included: When the first matching value is greater than the first upper limit threshold, it is determined that the welding detection of the first welding position is qualified; or, When the first matching value is less than the first lower limit threshold, determining that the welding detection of the first welding position is unqualified; or, When the target matching value is less than the first upper limit threshold, it is determined that the welding inspection of the first welding position is unqualified.

[0066] In a specific implementation, when the first matching value is greater than the first upper limit threshold, it indicates that the welding is good, and it is determined that the welding inspection at the first welding position is qualified.

[0067] In a specific implementation, when the first matching value is less than the first lower limit threshold, it indicates that there is a defect in the welding, that is, it can be determined that the welding detection at the first welding position is unqualified.

[0068] In a specific implementation, when the target matching value is less than the first upper limit threshold, it indicates that there is a defect in the welding, that is, it can be determined that the welding detection at the first welding position is unqualified.

[0069] It can be seen that the welding detection method in the automobile processing process described in the embodiment of the present application is applied to an industrial robot, which includes a visual system and a welding system. The first image for the first welding position is obtained by the visual system, and the first welding position is a welded position between the first automobile part and the second automobile part; the first image is segmented to obtain a first welding area image, a first feature is extracted according to the first welding area image to obtain a first feature set, a first reference feature set corresponding to the first welding position is obtained, and the first feature set is matched with the first reference feature set to obtain a first matching value; when the first matching value is within a first preset range, the first welding parameter and the first welding detection standard parameter corresponding to the first welding position are determined by the welding system, and the first welding detection standard parameter includes k standard high-frequency feature parameters, each of which is The standard high-frequency feature parameter corresponds to at least one high-frequency feature type of at least one high-frequency component part; k is a positive integer; the first preset range includes a first upper threshold value and a first lower threshold value; determine a first multi-scale decomposition algorithm corresponding to the first welding parameter and a first algorithm control parameter of the first multi-scale decomposition algorithm corresponding to the first welding detection standard parameter; perform multi-scale decomposition on the first welding area image according to the first multi-scale decomposition algorithm and the first algorithm control parameter to obtain a low-frequency component part and m high-frequency component parts; m is a positive integer; perform feature extraction on the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameter to obtain k high-frequency feature parameters; match the k high-frequency feature parameters with the k standard high-frequency feature parameters to obtain a second matching value; determine a target matching value according to the first matching value and the second matching value;When the target matching value is greater than or equal to the first upper limit threshold, it is determined that the welding inspection of the first welding position is qualified. Firstly, the characteristics of the qualified weld corresponding to the welding position can be obtained, and the first feature set is matched with the first reference feature set to obtain the first matching value, so as to preliminarily realize welding inspection from the appearance level. Secondly, when the first matching value is in the first preset range, it means that the welding appearance is general, and the first welding parameter and the first welding inspection standard parameter corresponding to the first welding position can be determined by the welding system. In this way, the welding parameters corresponding to the welding position and the corresponding welding inspection standard parameters can be obtained. Thirdly, not only the multi-scale decomposition algorithm corresponding to the welding process can be adopted, so as to ensure the deep extraction of the welding features corresponding to the welding process, but also the corresponding algorithm control is performed based on the welding inspection standard, which is helpful to deeply extract the welding features corresponding to the welding process, thereby helping to improve automobile processing. The welding detection efficiency in the process is improved. Fourthly, the low-frequency component part reflects the image subject, and the m high-frequency component parts reflect the image details. The low-frequency component part and the m high-frequency component parts reflect the degree of influence of the image subject on the image details, and the first welding detection standard parameter reflects the welding detection standard corresponding to the welding process, that is, the corresponding high-frequency feature parameters can be extracted based on the degree of influence of the image subject on the image details (the characteristics of the image itself) and the welding detection standard corresponding to the welding process, so that the welding features corresponding to the welding process can be deeply extracted. Fifthly, the welding quality is evaluated based on the welding features at the appearance level and the depth of the deep welding details, that is, the target matching value is determined according to the first matching value and the second matching value. When the target matching value is greater than or equal to the first upper limit threshold, it means that the welding is good, that is, it is determined that the welding detection of the first welding position is qualified, thereby helping to improve the welding detection efficiency in the automobile processing process. ;

[0070] In accordance with the above embodiment, please refer to Figure 2 , Figure 2 : is a schematic diagram of the structure of an industrial robot provided in an embodiment of the present application, the industrial robot includes a processor, a memory, a communication interface and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor. In the embodiment of the present application, the industrial robot includes a visual system and a welding system, and the program includes instructions for executing the following steps: Acquire a first image for a first welding position by the visual system, where the first welding position is a welded position between a first automobile component and a second automobile component; Performing region segmentation on the first image to obtain a first welding region image, performing first feature extraction based on the first welding region image to obtain a first feature set, acquiring a first reference feature set corresponding to the first welding position, and matching the first feature set with the first reference feature set to obtain a first matching value; When the first matching value is within a first preset range, the first welding parameter and the first welding detection standard parameter corresponding to the first welding position are determined by the welding system, the first welding detection standard parameter includes k standard high-frequency characteristic parameters, each standard high-frequency characteristic parameter corresponds to at least one high-frequency characteristic type of at least one high-frequency component part; k is a positive integer; the first preset range includes a first upper threshold and a first lower threshold; determining a first multi-scale decomposition algorithm corresponding to the first welding parameter and a first algorithm control parameter of the first multi-scale decomposition algorithm corresponding to the first welding detection standard parameter; The first welding area image is multi-scale decomposed according to the first multi-scale decomposition algorithm and the first algorithm control parameter to obtain a low-frequency component part and m high-frequency component parts; m is a positive integer; Extracting features of the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameter to obtain k kinds of high-frequency feature parameters; Matching the k kinds of high-frequency characteristic parameters with the k kinds of standard high-frequency characteristic parameters to obtain a second matching value; Determine a target matching value according to the first matching value and the second matching value; When the target matching value is greater than or equal to the first upper limit threshold, it is determined that the welding inspection of the first welding position is qualified.

[0071] Optionally, the program further includes instructions for executing the following steps: When the first matching value is greater than the first upper limit threshold, it is determined that the welding detection of the first welding position is qualified; or, When the first matching value is less than the first lower limit threshold, determining that the welding detection of the first welding position is unqualified; or, When the target matching value is less than the first upper limit threshold, it is determined that the welding inspection of the first welding position is unqualified.

[0072] Optionally, the first welding detection standard parameter further includes a standard matching value; in determining a target matching value according to the first matching value and the second matching value, the program includes instructions for performing the following steps: Determining a first relative deviation between the first upper threshold and the first matching value; When the first relative deviation is less than or equal to a preset relative deviation, determining a first deviation between the second matching value and the standard matching value; determining a first adjustment parameter corresponding to the first deviation; Determining the target matching value according to the first adjustment parameter and the first matching value; When the first relative deviation is greater than the preset relative deviation, determining a second relative deviation between the first matching value and the first lower threshold; Determine a target weight pair corresponding to the second relative deviation, the target weight pair comprising a first weight and a second weight, and a sum of the first weight and the second weight is 1; A weighted operation is performed according to the first matching value, the second matching value and the target weight to obtain the target matching value.

[0073] Optionally, in the aspect of extracting features of the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameter to obtain k kinds of high-frequency feature parameters, the program includes instructions for executing the following steps: Determining a first information entropy of the low-frequency component part; determining a second information entropy of the first welding area image; Determine a ratio between the first information entropy and the second information entropy to obtain a first ratio; When the first ratio is within a second preset range, the m high-frequency component parts are feature extracted according to the first welding detection standard parameters to obtain the k high-frequency feature parameters; the second preset range includes a second upper threshold and a second lower threshold.

[0074] Optionally, the program further includes instructions for executing the following steps: When the first ratio is greater than the second upper limit threshold, determining a third relative deviation between the first ratio and the second upper limit threshold, determining a first high-frequency image enhancement algorithm corresponding to the third relative deviation, determining a first mean between the first matching value and the second matching value; and determining a first control parameter corresponding to the first high-frequency image enhancement algorithm corresponding to the first mean; Performing image enhancement on the m high-frequency component parts according to the first high-frequency image enhancement algorithm and the first control parameter; The m high-frequency component parts after image enhancement are subjected to feature extraction according to the first welding detection standard parameters to obtain the k kinds of high-frequency feature parameters.

[0075] Optionally, the program further includes instructions for executing the following steps: When the first ratio is less than the second lower threshold, determining a fourth relative deviation between the second upper threshold and the first ratio, determining a first low-frequency image enhancement algorithm corresponding to the fourth relative deviation, determining a first difference between the first matching value and the first lower threshold, and determining a second control parameter of the first low-frequency image enhancement algorithm corresponding to the first difference; Performing image enhancement on the low-frequency component part according to the first low-frequency image enhancement algorithm and the second control parameter; Reconstructing the image based on the low-frequency component part and the m high-frequency component parts after image enhancement to obtain a second welding area image; Performing multi-scale decomposition on the second welding area image according to the first multi-scale decomposition algorithm and the first algorithm control parameter to obtain m target high-frequency component parts; The m target high-frequency component parts are subjected to feature extraction according to the first welding detection standard parameters to obtain the k types of high-frequency feature parameters.

[0076] It can be seen that the industrial robot described in the embodiment of the present application includes a vision system and a welding system. The first image for the first welding position is obtained by the vision system, and the first welding position is a welded position between the first automobile part and the second automobile part; the first image is segmented to obtain a first welding area image, a first feature is extracted according to the first welding area image to obtain a first feature set, a first reference feature set corresponding to the first welding position is obtained, and the first feature set is matched with the first reference feature set to obtain a first matching value; when the first matching value is within a first preset range, the first welding parameter and the first welding detection standard parameter corresponding to the first welding position are determined by the welding system, and the first welding detection standard parameter includes k standard high-frequency feature parameters, and each standard high-frequency feature parameter is k. At least one high-frequency feature type of at least one high-frequency component part; k is a positive integer; the first preset range includes a first upper threshold value and a first lower threshold value; determine a first multi-scale decomposition algorithm corresponding to the first welding parameter and a first algorithm control parameter of the first multi-scale decomposition algorithm corresponding to the first welding detection standard parameter; perform multi-scale decomposition on the first welding area image according to the first multi-scale decomposition algorithm and the first algorithm control parameter to obtain a low-frequency component part and m high-frequency component parts; m is a positive integer; perform feature extraction on the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameter to obtain k high-frequency feature parameters; match the k high-frequency feature parameters with the k standard high-frequency feature parameters to obtain a second matching value; determine a target matching value according to the first matching value and the second matching value;When the target matching value is greater than or equal to the first upper limit threshold, it is determined that the welding inspection of the first welding position is qualified. Firstly, the characteristics of the qualified weld corresponding to the welding position can be obtained, and the first feature set is matched with the first reference feature set to obtain the first matching value, so as to preliminarily realize welding inspection from the appearance level. Secondly, when the first matching value is in the first preset range, it means that the welding appearance is general, and the first welding parameter and the first welding inspection standard parameter corresponding to the first welding position can be determined by the welding system. In this way, the welding parameters corresponding to the welding position and the corresponding welding inspection standard parameters can be obtained. Thirdly, not only the multi-scale decomposition algorithm corresponding to the welding process can be adopted, so as to ensure the deep extraction of the welding features corresponding to the welding process, but also the corresponding algorithm control is performed based on the welding inspection standard, which is helpful to deeply extract the welding features corresponding to the welding process, thereby helping to improve automobile processing. The welding detection efficiency in the process is improved. Fourthly, the low-frequency component part reflects the image subject, and the m high-frequency component parts reflect the image details. The low-frequency component part and the m high-frequency component parts reflect the degree of influence of the image subject on the image details, and the first welding detection standard parameter reflects the welding detection standard corresponding to the welding process, that is, the corresponding high-frequency feature parameters can be extracted based on the degree of influence of the image subject on the image details (the characteristics of the image itself) and the welding detection standard corresponding to the welding process, so that the welding features corresponding to the welding process can be deeply extracted. Fifthly, the welding quality is evaluated based on the welding features at the appearance level and the depth of the deep welding details, that is, the target matching value is determined according to the first matching value and the second matching value. When the target matching value is greater than or equal to the first upper limit threshold, it means that the welding is good, that is, it is determined that the welding detection of the first welding position is qualified, thereby helping to improve the welding detection efficiency in the automobile processing process. ;

[0077] Figure 3 : is a functional unit composition block diagram of a welding detection system 300 in an automobile processing process involved in an embodiment of the present application. The welding detection system 300 in the automobile processing process is applied to an industrial robot. The industrial robot includes a visual system and a welding system. The welding detection system 300 in the automobile processing process includes: An acquisition unit 301 is used to acquire a first image for a first welding position through the visual system, where the first welding position is a welded position between a first automobile component and a second automobile component; A segmentation unit 302 is used to perform region segmentation on the first image to obtain a first welding region image, perform first feature extraction based on the first welding region image to obtain a first feature set, obtain a first reference feature set corresponding to the first welding position, and match the first feature set with the first reference feature set to obtain a first matching value; A determination unit 303 is configured to determine, by the welding system, a first welding parameter and a first welding detection standard parameter corresponding to the first welding position when the first matching value is within a first preset range, the first welding detection standard parameter including k standard high-frequency feature parameters, each standard high-frequency feature parameter corresponding to at least one high-frequency feature type of at least one high-frequency component part; k is a positive integer; the first preset range includes a first upper threshold and a first lower threshold; determine a first multi-scale decomposition algorithm corresponding to the first welding parameter and a first algorithm control parameter of the first multi-scale decomposition algorithm corresponding to the first welding detection standard parameter; A decomposition unit 304 is used to perform multi-scale decomposition on the first welding area image according to the first multi-scale decomposition algorithm and the first algorithm control parameter to obtain a low-frequency component part and m high-frequency component parts; m is a positive integer; An extraction unit 305 is used to extract features of the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameter to obtain k high-frequency feature parameters; A matching unit 306, configured to match the k kinds of high-frequency feature parameters with the k kinds of standard high-frequency feature parameters to obtain a second matching value; The determining unit 303 is used to determine a target matching value according to the first matching value and the second matching value; when the target matching value is greater than or equal to the first upper limit threshold, determine that the welding detection of the first welding position is qualified.

[0078] Optionally, the welding detection system 300 in the automobile processing process is also specifically used for: When the first matching value is greater than the first upper limit threshold, it is determined that the welding detection of the first welding position is qualified; or, When the first matching value is less than the first lower limit threshold, determining that the welding detection of the first welding position is unqualified; or, When the target matching value is less than the first upper limit threshold, it is determined that the welding inspection of the first welding position is unqualified.

[0079] Optionally, the first welding detection standard parameter further includes a standard matching value; in determining the target matching value according to the first matching value and the second matching value, the determining unit 303 is specifically used to: Determining a first relative deviation between the first upper threshold and the first matching value; When the first relative deviation is less than or equal to a preset relative deviation, determining a first deviation between the second matching value and the standard matching value; determining a first adjustment parameter corresponding to the first deviation; Determining the target matching value according to the first adjustment parameter and the first matching value; When the first relative deviation is greater than the preset relative deviation, determining a second relative deviation between the first matching value and the first lower threshold; Determine a target weight pair corresponding to the second relative deviation, the target weight pair comprising a first weight and a second weight, and a sum of the first weight and the second weight is 1; A weighted operation is performed according to the first matching value, the second matching value and the target weight to obtain the target matching value.

[0080] Optionally, in the aspect of extracting features of the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameter to obtain k kinds of high-frequency feature parameters, the extraction unit 305 is specifically used for: Determining a first information entropy of the low-frequency component part; determining a second information entropy of the first welding area image; Determine a ratio between the first information entropy and the second information entropy to obtain a first ratio; When the first ratio is within a second preset range, the m high-frequency component parts are feature extracted according to the first welding detection standard parameters to obtain the k high-frequency feature parameters; the second preset range includes a second upper threshold and a second lower threshold.

[0081] Optionally, the welding detection system 300 in the automobile processing process is also specifically used for: When the first ratio is greater than the second upper limit threshold, determining a third relative deviation between the first ratio and the second upper limit threshold, determining a first high-frequency image enhancement algorithm corresponding to the third relative deviation, determining a first mean between the first matching value and the second matching value; and determining a first control parameter corresponding to the first high-frequency image enhancement algorithm corresponding to the first mean; Performing image enhancement on the m high-frequency component parts according to the first high-frequency image enhancement algorithm and the first control parameter; The m high-frequency component parts after image enhancement are subjected to feature extraction according to the first welding detection standard parameters to obtain the k kinds of high-frequency feature parameters.

[0082] Optionally, the welding detection system 300 in the automobile processing process is also specifically used for: When the first ratio is less than the second lower threshold, determining a fourth relative deviation between the second upper threshold and the first ratio, determining a first low-frequency image enhancement algorithm corresponding to the fourth relative deviation, determining a first difference between the first matching value and the first lower threshold, and determining a second control parameter of the first low-frequency image enhancement algorithm corresponding to the first difference; Performing image enhancement on the low-frequency component part according to the first low-frequency image enhancement algorithm and the second control parameter; Reconstructing the image based on the low-frequency component part and the m high-frequency component parts after image enhancement to obtain a second welding area image; Performing multi-scale decomposition on the second welding area image according to the first multi-scale decomposition algorithm and the first algorithm control parameter to obtain m target high-frequency component parts; The m target high-frequency component parts are subjected to feature extraction according to the first welding detection standard parameters to obtain the k types of high-frequency feature parameters.

[0083] It can be seen that the welding detection system in the automobile processing process described in the embodiment of the present application is applied to an industrial robot, which includes a vision system and a welding system. The first image for the first welding position is obtained by the vision system, and the first welding position is a welded position between the first automobile part and the second automobile part; the first image is segmented to obtain a first welding area image, a first feature is extracted according to the first welding area image to obtain a first feature set, a first reference feature set corresponding to the first welding position is obtained, and the first feature set is matched with the first reference feature set to obtain a first matching value; when the first matching value is within a first preset range, the first welding parameter and the first welding detection standard parameter corresponding to the first welding position are determined by the welding system, and the first welding detection standard parameter includes k standard high-frequency feature parameters, each of which is The standard high-frequency feature parameter corresponds to at least one high-frequency feature type of at least one high-frequency component part; k is a positive integer; the first preset range includes a first upper threshold value and a first lower threshold value; determine a first multi-scale decomposition algorithm corresponding to the first welding parameter and a first algorithm control parameter of the first multi-scale decomposition algorithm corresponding to the first welding detection standard parameter; perform multi-scale decomposition on the first welding area image according to the first multi-scale decomposition algorithm and the first algorithm control parameter to obtain a low-frequency component part and m high-frequency component parts; m is a positive integer; perform feature extraction on the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameter to obtain k high-frequency feature parameters; match the k high-frequency feature parameters with the k standard high-frequency feature parameters to obtain a second matching value; determine a target matching value according to the first matching value and the second matching value;When the target matching value is greater than or equal to the first upper limit threshold, it is determined that the welding inspection of the first welding position is qualified. Firstly, the characteristics of the qualified weld corresponding to the welding position can be obtained, and the first feature set is matched with the first reference feature set to obtain the first matching value, so as to preliminarily realize welding inspection from the appearance level. Secondly, when the first matching value is in the first preset range, it means that the welding appearance is general, and the first welding parameter and the first welding inspection standard parameter corresponding to the first welding position can be determined by the welding system. In this way, the welding parameters corresponding to the welding position and the corresponding welding inspection standard parameters can be obtained. Thirdly, not only the multi-scale decomposition algorithm corresponding to the welding process can be adopted, so as to ensure the deep extraction of the welding features corresponding to the welding process, but also the corresponding algorithm control is performed based on the welding inspection standard, which is helpful to deeply extract the welding features corresponding to the welding process, thereby helping to improve automobile processing. The welding detection efficiency in the process is improved. Fourthly, the low-frequency component part reflects the image subject, and the m high-frequency component parts reflect the image details. The low-frequency component part and the m high-frequency component parts reflect the degree of influence of the image subject on the image details, and the first welding detection standard parameter reflects the welding detection standard corresponding to the welding process, that is, the corresponding high-frequency feature parameters can be extracted based on the degree of influence of the image subject on the image details (the characteristics of the image itself) and the welding detection standard corresponding to the welding process, so that the welding features corresponding to the welding process can be deeply extracted. Fifthly, the welding quality is evaluated based on the welding features at the appearance level and the depth of the deep welding details, that is, the target matching value is determined according to the first matching value and the second matching value. When the target matching value is greater than or equal to the first upper limit threshold, it means that the welding is good, that is, it is determined that the welding detection of the first welding position is qualified, thereby helping to improve the welding detection efficiency in the automobile processing process. ;

[0084] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method recorded in the above method embodiments, and the above computer includes an industrial robot.

[0085] The present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an industrial robot.

[0086] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0087] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0088] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0089] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0091] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0092] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0093] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A welding detection method in an automobile processing process, characterized in that: Applied to an industrial robot, the industrial robot includes a vision system and a welding system, and the method includes: Acquire a first image for a first welding position by the visual system, where the first welding position is a welded position between a first automobile component and a second automobile component; Performing region segmentation on the first image to obtain a first welding region image, performing first feature extraction based on the first welding region image to obtain a first feature set, acquiring a first reference feature set corresponding to the first welding position, and matching the first feature set with the first reference feature set to obtain a first matching value; When the first matching value is within a first preset range, the first welding parameter and the first welding detection standard parameter corresponding to the first welding position are determined by the welding system, the first welding detection standard parameter includes k standard high-frequency characteristic parameters, each standard high-frequency characteristic parameter corresponds to at least one high-frequency characteristic type of at least one high-frequency component part; k is a positive integer; the first preset range includes a first upper threshold and a first lower threshold; determining a first multi-scale decomposition algorithm corresponding to the first welding parameter and a first algorithm control parameter of the first multi-scale decomposition algorithm corresponding to the first welding detection standard parameter; The first welding area image is multi-scale decomposed according to the first multi-scale decomposition algorithm and the first algorithm control parameter to obtain a low-frequency component part and m high-frequency component parts; m is a positive integer; Extracting features of the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameter to obtain k kinds of high-frequency feature parameters; Matching the k kinds of high-frequency characteristic parameters with the k kinds of standard high-frequency characteristic parameters to obtain a second matching value; Determine a target matching value according to the first matching value and the second matching value; When the target matching value is greater than the first upper limit threshold, it is determined that the welding inspection of the first welding position is qualified.

2. The method according to claim 1, characterized in that The method further comprises: When the first matching value is greater than the first upper limit threshold, it is determined that the welding detection of the first welding position is qualified; or, When the first matching value is less than the first lower limit threshold, determining that the welding detection of the first welding position is unqualified; or, When the target matching value is less than the first upper limit threshold, it is determined that the welding inspection of the first welding position is unqualified.

3. The method according to claim 1 or 2, characterized in that: The first welding detection standard parameter also includes a standard matching value; and determining a target matching value according to the first matching value and the second matching value includes: Determining a first relative deviation between the first upper threshold and the first matching value; When the first relative deviation is less than or equal to a preset relative deviation, determining a first deviation between the second matching value and the standard matching value; determining a first adjustment parameter corresponding to the first deviation; Determining the target matching value according to the first adjustment parameter and the first matching value; When the first relative deviation is greater than the preset relative deviation, determining a second relative deviation between the first matching value and the first lower threshold; Determine a target weight pair corresponding to the second relative deviation, the target weight pair comprising a first weight and a second weight, and a sum of the first weight and the second weight is 1; A weighted operation is performed according to the first matching value, the second matching value and the target weight to obtain the target matching value.

4. The method according to claim 1 or 2, characterized in that: The extracting features of the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameter to obtain k kinds of high-frequency feature parameters includes: Determining a first information entropy of the low-frequency component part; determining a second information entropy of the first welding area image; Determine a ratio between the first information entropy and the second information entropy to obtain a first ratio; When the first ratio is within a second preset range, the m high-frequency component parts are feature extracted according to the first welding detection standard parameters to obtain the k high-frequency feature parameters; the second preset range includes a second upper threshold and a second lower threshold.

5. The method according to claim 4, characterized in that The method further comprises: When the first ratio is greater than the second upper limit threshold, determining a third relative deviation between the first ratio and the second upper limit threshold, determining a first high-frequency image enhancement algorithm corresponding to the third relative deviation, determining a first mean between the first matching value and the second matching value; and determining a first control parameter corresponding to the first high-frequency image enhancement algorithm corresponding to the first mean; Performing image enhancement on the m high-frequency component parts according to the first high-frequency image enhancement algorithm and the first control parameter; The m high-frequency component parts after image enhancement are subjected to feature extraction according to the first welding detection standard parameters to obtain the k kinds of high-frequency feature parameters.

6. A welding detection system in the automobile processing process, characterized in that: Applied to an industrial robot, the industrial robot includes a visual system and a welding system, and the welding detection system in the automobile processing process includes: An acquisition unit, configured to acquire a first image for a first welding position through the visual system, where the first welding position is a welded position between a first automobile component and a second automobile component; a segmentation unit, configured to perform region segmentation on the first image to obtain a first welding region image, perform first feature extraction based on the first welding region image to obtain a first feature set, obtain a first reference feature set corresponding to the first welding position, and match the first feature set with the first reference feature set to obtain a first matching value; a determination unit, configured to determine, by the welding system, a first welding parameter and a first welding detection standard parameter corresponding to the first welding position when the first matching value is within a first preset range, the first welding detection standard parameter including k standard high-frequency feature parameters, each standard high-frequency feature parameter corresponding to at least one high-frequency feature type of at least one high-frequency component part; k is a positive integer; the first preset range includes a first upper threshold and a first lower threshold; determine a first multi-scale decomposition algorithm corresponding to the first welding parameter and a first algorithm control parameter of the first multi-scale decomposition algorithm corresponding to the first welding detection standard parameter; a decomposition unit, configured to perform multi-scale decomposition on the first welding region image according to the first multi-scale decomposition algorithm and the first algorithm control parameter to obtain a low-frequency component part and m high-frequency component parts; m is a positive integer; an extraction unit, configured to extract features of the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameter, so as to obtain k kinds of high-frequency feature parameters; A matching unit, configured to match the k kinds of high-frequency characteristic parameters with the k kinds of standard high-frequency characteristic parameters to obtain a second matching value; The determination unit is used to determine a target matching value according to the first matching value and the second matching value; when the target matching value is greater than or equal to the first upper limit threshold, determine that the welding detection of the first welding position is qualified.

7. The welding detection system in the automobile processing process according to claim 6, characterized in that: The welding detection system in the automobile processing process is also specifically used for: When the first matching value is greater than the first upper limit threshold, it is determined that the welding detection of the first welding position is qualified; or, When the first matching value is less than the first lower limit threshold, determining that the welding detection of the first welding position is unqualified; or, When the target matching value is less than the first upper limit threshold, it is determined that the welding inspection of the first welding position is unqualified.

8. The welding detection system in the automobile processing process according to claim 6 or 7, characterized in that: The first welding detection standard parameter also includes a standard matching value; in determining the target matching value according to the first matching value and the second matching value, the determining unit is specifically used to: Determining a first relative deviation between the first upper threshold and the first matching value; When the first relative deviation is less than or equal to a preset relative deviation, determining a first deviation between the second matching value and the standard matching value; determining a first adjustment parameter corresponding to the first deviation; Determining the target matching value according to the first adjustment parameter and the first matching value; When the first relative deviation is greater than the preset relative deviation, determining a second relative deviation between the first matching value and the first lower threshold; Determine a target weight pair corresponding to the second relative deviation, the target weight pair comprising a first weight and a second weight, and a sum of the first weight and the second weight is 1; A weighted operation is performed according to the first matching value, the second matching value and the target weight to obtain the target matching value.

9. The welding detection system in the automobile processing process according to claim 6 or 7, characterized in that: In the aspect of extracting features of the m high-frequency component parts according to the low-frequency component part, the m high-frequency component parts and the first welding detection standard parameter to obtain k kinds of high-frequency feature parameters, the extraction unit is specifically used for: Determining a first information entropy of the low-frequency component part; determining a second information entropy of the first welding area image; Determine a ratio between the first information entropy and the second information entropy to obtain a first ratio; When the first ratio is within a second preset range, the m high-frequency component parts are feature extracted according to the first welding detection standard parameters to obtain the k high-frequency feature parameters; the second preset range includes a second upper threshold and a second lower threshold.

10. The welding detection system in the automobile processing process according to claim 9, characterized in that: The welding detection system in the automobile processing process is also specifically used for: When the first ratio is greater than the second upper limit threshold, determining a third relative deviation between the first ratio and the second upper limit threshold, determining a first high-frequency image enhancement algorithm corresponding to the third relative deviation, and determining a first mean value between the first matching value and the second matching value; determining a first control parameter corresponding to the first high-frequency image enhancement algorithm corresponding to the first mean value; Performing image enhancement on the m high-frequency component parts according to the first high-frequency image enhancement algorithm and the first control parameter; The m high-frequency component parts after image enhancement are subjected to feature extraction according to the first welding detection standard parameters to obtain the k kinds of high-frequency feature parameters.

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