Solder defect detection method, detection device, computer equipment and storage medium

Abstract

This application discloses a method, apparatus, computer equipment, and computer-readable storage medium for detecting solder defects. The method includes: acquiring an original 3D image of a solder area; converting the original 3D image into a 2D image; processing the 2D image to obtain outer edge information of the solder area; dividing the solder area into different detection zones in the original 3D image based on the outer edge information; determining whether solder defects exist within the detection zones; and if solder defects exist within the detection zones, determining the information of the solder defects. This method, by acquiring an original 3D image of a solder area, dividing the solder area based on the outer edge information of the 2D image converted from the original 3D image, and then determining the solder defect information in different zones, achieves convenient, rapid, and accurate location of defect locations, improves detection efficiency, and compared to manual inspection, requires less manpower, is lower in cost, and has higher reliability.

Description

Technical Field

[0001] This application relates to the field of surface defect detection technology, and in particular to a method, detection device, computer equipment, and computer-readable storage medium for detecting solder defects. Background Technology

[0002] Defect detection technology refers to the technique of detecting defects such as surface spots, pits, scratches, color differences, defects, and internal structures of test samples to obtain information such as the depth, size, outline, and type of defects on or inside the sample. Current defect detection technologies generally rely on manual inspection; however, manual inspection requires a large investment of manpower, is costly and inefficient, and is prone to missed or false detections. Summary of the Invention

[0003] This application provides a method, apparatus, computer equipment, and computer-readable storage medium for detecting solder defects.

[0004] The solder defect detection method of this application includes:

[0005] Obtain the original 3D image of the solder area;

[0006] Convert the original 3D image into a 2D image;

[0007] Process the 2D image to obtain the outer edge information of the solder area;

[0008] Based on the outer edge information, the solder area is divided into different detection zones in the original 3D image;

[0009] Determine whether solder defects exist within the detection area;

[0010] If a solder defect exists in the detection area, the information of the solder defect is determined.

[0011] The solder defect detection method of this application acquires the original 3D image of the solder area, then divides the solder area based on the outer edge information of the 2D image converted from the original 3D image, and then determines the solder defect information of different areas. This achieves convenient, fast and accurate positioning of defect locations, improves detection efficiency, and compared with manual inspection, it does not require a large amount of manpower, has low cost and good reliability.

[0012] In some implementations, converting the original 3D image into a 2D image includes:

[0013] According to the grayscale mapping relationship, the 3D image with a first predetermined number of bits is converted into the 2D image with a second predetermined number of bits, wherein the first predetermined number of bits is greater than the second predetermined number of bits.

[0014] In some implementations, processing the 2D image to obtain the outer edge information of the solder area includes:

[0015] The 2D image is processed using a predetermined method to obtain a clear solder area image, the predetermined processing method including at least one of contrast enhancement, binarization, and morphological processing;

[0016] Perform a Blob operation on the solder area image to obtain the outer edge information of the solder area.

[0017] In some implementations, determining whether a solder defect exists within the detection area includes:

[0018] Based on the detection area, a new 3D image of the solder area is re-acquired;

[0019] The new 3D image is compared with the original 3D image to highlight the image of the detection area;

[0020] The presence of solder defects is determined based on the image of the highlighted detection area.

[0021] In some implementations, determining whether solder defects exist within the detection area before comparing the new 3D image with the original 3D image further includes:

[0022] Interpolate the coordinates of the new 3D image to the positions in the original 3D image where there is no data;

[0023] The center of the new 3D image is completely aligned with the center of the original 3D image.

[0024] In some embodiments, the detection method further includes:

[0025] Upload the information about the solder defect.

[0026] In some embodiments, the detection method further includes:

[0027] Establish a communication connection with the 3D scanning camera;

[0028] Control the 3D scanning camera to operate so that the 3D scanning camera can acquire the original 3D image.

[0029] The detection device according to the embodiments of this application includes:

[0030] The acquisition module is used to acquire the original 3D image of the solder area;

[0031] A conversion module is used to convert the original 3D image into a 2D image;

[0032] The processing module is used to process the 2D image to obtain the outer edge information of the solder area;

[0033] The segmentation module, based on the outer edge information, divides the solder area into different detection zones in the original 3D image;

[0034] The first determining module determines whether solder defects exist in the detection area;

[0035] The second determining module determines the information of the solder defect if a solder defect exists in the detection area.

[0036] The detection device of this application acquires the original 3D image of the solder area through each module, and then divides the solder area based on the outer edge information of the 2D image converted from the original 3D image. Then, it determines the solder defect information of different areas, which realizes convenient, fast and accurate positioning of defect parts, improves detection efficiency, and compared with manual detection, it does not require a lot of manpower, has low cost and good reliability.

[0037] The computer device according to the embodiments of this application includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the detection method described in any of the above embodiments.

[0038] The non-volatile computer-readable storage medium of the computer-executable instructions of this application, when executed by one or more processors, causes the processors to perform the detection method described in any of the above embodiments.

[0039] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0040] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, wherein:

[0041] Figure 1 This is a schematic flowchart of the solder defect detection method according to the embodiments of this application;

[0042] Figure 2 This is a schematic diagram of the detection device according to an embodiment of this application;

[0043] Figure 3 This is a schematic diagram of the computer device according to an embodiment of this application;

[0044] Figure 4 This is a schematic flowchart of the solder defect detection method according to the embodiments of this application;

[0045] Figure 5 It is a 3D image of the solder defect detection method according to the embodiments of this application, which uses the Blob result to divide the solder detection area;

[0046] Figure 6 This is a schematic flowchart of the solder defect detection method according to the embodiments of this application;

[0047] Figure 7 This is a schematic flowchart of the solder defect detection method according to the embodiments of this application;

[0048] Figure 8 This is a schematic diagram showing that the solder defect detection method of the present application detects a solder defect with a raised geometric shape.

[0049] Figure 9 This is a schematic diagram showing that the solder defect detection method of the present application detects a concave geometry of the solder defect.

[0050] Figure 10 This is a schematic diagram showing that the solder defect detection method of the embodiment of this application detects a solder defect with a long strip-shaped geometric shape.

[0051] Figure 11 This is a schematic diagram of the solder defect detection method according to the embodiments of this application before using enhanced display of solder defects;

[0052] Figure 12 This is a schematic diagram of the solder defect detection method according to the embodiments of this application after enhancing the display of one type of solder defect;

[0053] Figure 13 This is a schematic diagram of the solder defect detection method according to the embodiments of this application after enhancing the display of another solder defect;

[0054] Figure 14 This is a flowchart illustrating the solder defect detection method according to an embodiment of this application.

[0055] Explanation of key component symbols:

[0056] The system includes a detection device 100, an acquisition module 11, a conversion module 12, a processing module 13, a division module 14, a first determination module 15, a second determination module 16, a computer device 200, a memory 210, and a processor 220. Detailed Implementation

[0057] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0058] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0059] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0060] The following disclosure provides many different embodiments or examples for implementing different structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. In addition, various specific examples of processes and materials are provided in this application, but those skilled in the art will recognize the application of other processes and / or the use of other materials.

[0061] Please see Figure 1 The solder defect detection method of this application includes:

[0062] S10: Obtain the original 3D image of the solder area;

[0063] S20: Convert the original 3D image to a 2D image;

[0064] S30: Process the 2D image to obtain information about the outer edge of the solder area;

[0065] S40: Based on the outer edge information, the solder area is divided into different detection zones in the original 3D image;

[0066] S50: Determine if solder defects exist in the inspection area;

[0067] S60: If a solder defect exists in the detection area, determine the information about the solder defect.

[0068] Please see Figure 2 The detection device 100 of this application includes an acquisition module 11, a conversion module 12, a processing module 13, a division module 14, a first determination module 15, and a second determination module 16. The solder defect detection method in this application can be implemented by the detection device 100.

[0069] Specifically, the acquisition module 11 is used to acquire the original 3D image of the solder area; the conversion module 12 is used to convert the original 3D image into a 2D image; the processing module 13 is used to process the 2D image to obtain the outer edge information of the solder area; the division module 14 is used to divide the solder area into different detection areas in the original 3D image based on the outer edge information; the first determination module 15 is used to determine whether there is a solder defect in the detection area; and the second determination module 16 is used to determine the information of the solder defect if there is a solder defect in the detection area.

[0070] Please see Figure 3 The computer device 200 of this application includes a memory 210 and a processor 220. The memory 210 stores a computer program, and the processor 220 executes the computer program to implement the solder defect detection method in steps S10, S20, S30, S40, S50 and S60.

[0071] The solder defect detection method of this application acquires the original 3D image of the solder area through the detection device 100, and then divides the solder area based on the outer edge information of the 2D image converted from the original 3D image, and then determines the solder defect information of different areas. This achieves convenient, fast and accurate positioning of defect locations, improves detection efficiency, and compared with manual detection, it does not require a large amount of manpower, has low cost and good reliability.

[0072] Specifically, the detection method of this application can be applied to the solder detection of battery casings such as lithium batteries. The detection device 100 can be a device integrating communication, image acquisition, and image processing functions.

[0073] For example, the inspection device 100 may include industrial vision software. Industrial vision technology was initially used in automated production using 2D vision technology. However, since 2D vision can usually only solve problems on a two-dimensional plane, it is difficult to be effective for objects with height information, such as products involving curved surfaces or arcs. This has promoted the rise of 3D vision. Compared to 2D vision, 3D vision is less sensitive to changes in ambient light, has higher accuracy and reliability, and can detect fast-moving targets in the production line and obtain information such as shape, color contrast, and spatial coordinates.

[0074] 3D vision can meet more industrial application needs that were previously difficult to fulfill with 2D vision, thus complementing 2D vision. Industrial 3D vision mainly focuses on three major application areas: size and defect detection, intelligent manufacturing, and autonomous navigation. Industrial 3D vision technology can empower the entire production line in the industrial sector.

[0075] In step S10, the original 3D image of the solder area of ​​the lithium battery can be obtained by a camera. Preferably, an SR8060 camera can be used to acquire the solder area. This camera can be triggered by an encoder to achieve fast and high-precision image acquisition. A communication protocol can also be established to control the camera's acquisition start position, acquisition end position, and trigger duration.

[0076] The moving mechanism can be a movable mechanism used to set up or fix the lithium battery and its solder area. The specific operation of data acquisition can be achieved by creating relative motion between the moving mechanism and the camera, and by controlling the camera to acquire thousands of laser contour data through the signal output by the encoder during the movement of the moving mechanism. The laser contour data is then converted into three-dimensional coordinate values ​​in a pre-calibrated coordinate system of the 3D camera according to the algorithm inside the camera. The camera can be installed at a certain angle to the solder area of ​​the lithium battery to facilitate complete imaging of the solder area.

[0077] In step S20, the conversion module 12 can convert the original 3D image acquired in step S10 into a 2D image through data mapping, data conversion and other methods; in step S30, the 2D image obtained in step S20 can be processed to obtain the solder area, and then the algorithm can be used to calculate the outer edge structure information of the solder area.

[0078] In step S40, based on the outer edge information obtained in step S30, the segmentation module 14 can divide the solder area on the original 3D image into different detection areas; then step S50 can be taken, whereby the first determination module 15 can use an algorithm to determine whether there is a solder defect in the detection area; if there is a solder defect in the detection area, the second determination module 16 can take step S60 to determine the information of the solder defect in the corresponding area.

[0079] In some implementations, converting the original 3D image to a 2D image (step S20) includes:

[0080] S21: Based on the grayscale mapping relationship, convert the 3D image with a first predetermined number of bits into a 2D image with a second predetermined number of bits, wherein the first predetermined number of bits is greater than the second predetermined number of bits.

[0081] Please see Figure 2 In some embodiments, the conversion module 12 is used to convert a 3D image with a first predetermined number of bits into a 2D image with a second predetermined number of bits according to the grayscale mapping relationship, wherein the first predetermined number of bits is greater than the second predetermined number of bits.

[0082] Please see Figure 3 In some implementations, the processor 220 is used to convert a 3D image with a first predetermined number of bits into a 2D image with a second predetermined number of bits according to a grayscale mapping relationship, wherein the first predetermined number of bits is greater than the second predetermined number of bits.

[0083] Therefore, using grayscale mapping to convert the original 3D image into a 2D image can reduce image noise, improve image quality, and facilitate subsequent detection area division.

[0084] Specifically, the first predetermined bit 3D image can be a 16-bit 3D image, and the second predetermined bit image can be a 2D image with fewer than 16 bits, for example, an 8-bit 2D image. The principle of the grayscale mapping relationship for converting the first predetermined bit 3D image to the second predetermined bit 2D image can be as follows:

[0085] We can first assume a grayscale mapping relationship between the first pre-preset 3D image and the second pre-preset 2D image, and then establish a coordinate system. For example, the X-axis of the coordinate system can represent the grayscale values ​​of the first pre-preset 3D image, and the Y-axis can represent the grayscale values ​​of the second pre-preset 2D image. Then, we take the original reference point coordinates of the first pre-preset 3D image as (x1, y1) and the original reference point coordinates of the second pre-preset 3D image as (x2, y2). The mapping is then defined as follows:

[0086] (x1,y1)→(x`1,y`1),

[0087] Where, x`1=x1+(x2-x1)×a, y`1=255×f(a).

[0088] (x2,y2)→(x`2,y`2), where,

[0089] ``

[0090] x2=x2+(x2-x1)×b, y2=255×f(b).

[0091] It should be noted that the values ​​of a and b are in the range of [0,1], and should be set according to the actual use case; f(a) and f(b) are internal functions of the algorithm, which are controlled by the values ​​of a and b; this algorithm can effectively remove noise caused by camera optical imaging.

[0092] Please see Figure 4 In some implementations, processing the 2D image to obtain the outer edge information of the solder area (step S30) includes:

[0093] S31: Process a 2D image using a predetermined method to obtain a clear solder area image, wherein the predetermined processing method includes at least one of contrast enhancement, binarization, and morphological processing.

[0094] S32: Perform Blob operation on the solder area image to obtain the outer edge information of the solder area.

[0095] Please see Figure 2 In some embodiments, the processing module 13 is used to process the 2D image using a predetermined method to obtain a clear solder area image, the predetermined processing method including at least one of contrast enhancement, binarization, and morphological processing; and to perform Blob operation on the solder area image to obtain the outer edge information of the solder area.

[0096] Please see Figure 3 In some embodiments, the processor 220 is used to process the 2D image using a predetermined method to obtain a clear solder area image, the predetermined processing method including at least one of contrast enhancement, binarization, and morphological processing; and to perform a Blob operation on the solder area image to obtain the outer edge information of the solder area.

[0097] Thus, processing the 2D image in a predetermined manner can make the image of the solder area clearer, and using Blob operation can effectively obtain the geometric information of the solder area, thereby accurately obtaining the outer edge information of the solder area.

[0098] Specifically, the processing module 13 can first perform step S31 in step S30, where the preprocessing can be performed sequentially. First, an image enhancement algorithm based on grayscale histogram equalization can be used to adjust the contrast of the 2D image, thereby increasing the overall contrast of the solder area in the 2D image. Then, the Canny algorithm can be used to detect the edges of the entire solder area to obtain a binarized image. Finally, a morphological algorithm can be used to erode the 2D image to remove the "isolated" data within the solder area.

[0099] The principle of using morphological algorithms to erode images can be illustrated with an example. Suppose a morphological structuring element is B. After translating it by a, we get Ba. If Ba is contained in X, we record this point a. The set of all points a that satisfy this condition is called the result of X being eroded by B, which can be expressed by the formula: E(X)={a|Ba∈X}=XB.

[0100] Therefore, a clear image of the solder area can be obtained through the above preprocessing method.

[0101] Then, step S32 is performed to use the Blob algorithm to obtain the entire solder area in the preprocessed 2D image. The geometric information of the solder area is also obtained, including the outer edge information of the solder area.

[0102] Then, after step S32, step S40 can continue. Based on the outer edge information of the solder area, the solder detection area can be divided. For example, based on the Blob information obtained by the Blob algorithm in step S32, the Blob information may include the bounding rectangle parameters of the Blob, the coordinates of the outermost point of the Blob, etc. The coordinates of the outermost point of the Blob can be used to divide the solder area in the 3D image data. Figure 5 The diagram shows how the solder detection area is divided using Blob results. The area at the top and bottom is the corner detection area A, and the area between the two corner detection areas is the normal detection area B.

[0103] Please see Figure 6 In some implementations, determining whether solder defects exist within the detection area (step S50) includes:

[0104] S51: Based on the detection area, re-acquire a new 3D image of the solder area;

[0105] S52: Compare the new 3D image with the original 3D image to highlight the image of the detection area;

[0106] S53: Determine whether solder defects exist based on the image of the highlighted detection area.

[0107] Please see Figure 2 In some embodiments, the first determining module 15 is used to reacquire a new 3D image of the solder area based on the detection area; and to compare the new 3D image with the original 3D image to highlight the image of the detection area; and to determine whether a solder defect exists based on the image of the highlighted detection area.

[0108] Please see Figure 3In some embodiments, the processor 220 is used to reacquire a new 3D image of the solder area based on the detection area; and to compare the new 3D image with the original 3D image to highlight the image of the detection area; and to determine whether a solder defect exists based on the image of the highlighted detection area.

[0109] Therefore, by acquiring a new 3D image of the solder area and comparing it with the original 3D image, the solder defects in the divided inspection area can be highlighted accurately and conveniently, reducing misjudgments and missed detections compared to manual inspection.

[0110] Specifically, based on the different detection areas divided within the original 3D image in step S40, different resampling parameters are used to resample the 3D image data to obtain the corresponding resampled image, that is, to re-acquire a new 3D image of the solder area.

[0111] This can also be understood as using a defect detection algorithm to detect defects within each detection area. For example, we can assume that the original 3D image is D(x i ,y i ,z i A resampled image D`(x) can be obtained by resampling the original image along the X or Y direction, with a size of m×n. i `,y i `,z i This refers to new 3D images.

[0112] Then, as in step S52, the new 3D image and the original 3D image are aligned and compared to obtain an image highlighting the detection area. Then, in step S53, the presence of solder defects within the detection area is determined based on the image of the highlighted detection area. For example, the original 3D image D(x i ,y i ,z i ) and the sampled image D`(x i `,y i `,z i After comparison, the image obtained containing solder defects can be denoted as I(x). i ,y i ,z i ).

[0113] Please see Figure 7 In some implementations, determining whether solder defects exist in the detection area before comparing the new 3D image with the original 3D image (step S50) further includes:

[0114] S54: Interpolate the coordinates of the new 3D image to the positions in the original 3D image where there is no data;

[0115] S55: Make the center of the new 3D image completely coincide with the center of the original 3D image.

[0116] Please see Figure 2 In some embodiments, the first determining module 15 is further configured to perform interpolation calculations on the positions in the new 3D image and the original 3D image where there is no data at the corresponding coordinates; and to make the center of the new 3D image completely coincide with that of the original 3D image.

[0117] Please see Figure 3 In some embodiments, the processor 220 is used to perform interpolation calculations on the positions in the new 3D image that have no data at the corresponding coordinates of the original 3D image; and to make the center of the new 3D image completely coincide with that of the original 3D image.

[0118] Thus, interpolating the coordinates of the new 3D image with the original 3D image at the locations where there is no data ensures that the size of the resampled 3D image remains unchanged compared to the original 3D image; and making the centers of the new 3D image and the original 3D image completely coincide allows the new 3D image and the original 3D image to be aligned with each other, which is beneficial for comparison.

[0119] Specifically, before comparing the new 3D image with the original 3D image, to ensure that the size of the new 3D image is the same as that of the original 3D image, step S54 can be taken to interpolate the positions in the new 3D image and the original 3D image that have no data at corresponding coordinates. Then, as in step S55, to ensure that the centers of the new 3D image and the original 3D image completely coincide, an alignment operation between the centers of the original 3D image and the new 3D image is performed in the algorithm for converting the original 3D image into the new 3D image.

[0120] In some implementations, the detection method further includes:

[0121] S70: Upload information about solder defects.

[0122] Please see Figure 2 In some embodiments, the detection device 100 is used to upload information about solder defects. See also... Figure 3 In some implementations, processor 220 is used to upload information about solder defects.

[0123] In this way, by uploading the defect information detected in the solder area of ​​the divided inspection zone, the detection method can provide users with intuitive defect information and improve the user experience;

[0124] Specifically, if a solder defect is detected in the detection area in step S60 and the information of the solder defect is determined, step S70 can be continued to upload the information of the solder defect. The information of the solder defect can be aggregate size information, such as the length and width of the bounding rectangle of the defect area, the aspect ratio of the defect, the density of the defect, the degree of scratches on the defect, etc.

[0125] Combination Figures 8-10 To understand further, Figure 8 The solder defects shown include four types of raised defects. Figure 9 The solder defects shown have two geometric shapes, both of which are concave. Figure 10 The solder defects shown have two geometric shapes, both of which are elongated strips.

[0126] Furthermore, the detection method can enhance the display of defects under different geometric shapes by setting different parameters, making the information about solder defects clearer and more explicit. For example... Figure 11 This is the image before enhancement of solder defects in a 3D image. The ellipse contains defect type 01, and the rectangle contains defect type 02. When defect 01 is enhanced and defect 02 is weakened, the enhanced image is as follows: Figure 12 As shown, display defect 01 is more clearly visible. Furthermore, by enhancing display defect 02 and weakening display defect 01, the enhanced image can be as follows: Figure 13 As shown, defect 02 is more clearly visible.

[0127] In summary, the geometric dimensions of these solder defects can be sent to the automated workstation in a specific format using a communication protocol. The workstation can then use this information to identify defective materials.

[0128] Please see Figure 14 In some implementations, the detection method further includes:

[0129] S08: Establish a communication connection with the 3D scanning camera;

[0130] S09: Control the 3D scanning camera to enable it to acquire raw 3D images.

[0131] Please see Figure 2 In some embodiments, the detection device 100 is used to establish a communication connection with the 3D scanning camera and to control the operation of the 3D scanning camera so that the 3D scanning camera can acquire raw 3D images.

[0132] Please see Figure 3 In some embodiments, the processor 220 is used to establish a communication connection with the 3D scanning camera and to control the operation of the 3D scanning camera so that the 3D scanning camera acquires raw 3D images.

[0133] In this way, the detection method establishes a connection with the 3D scanning camera through communication and controls the 3D scanning camera to acquire the original 3D image of the solder material. It does not require manual operation, is fast and automatic, and thus improves detection efficiency.

[0134] Specifically, before step S10, the detection method can first take step S08, which uses professional industrial vision software to establish communication between the trigger signal output by the encoder on the moving mechanism in the automated workstation and the 3D scanning camera. Then, as in step S09, the starting position, scanning frequency, and ending position of the 3D scanning camera are further precisely controlled to acquire a 3D image of the actual size of the lithium battery soldering area. The acquired 3D image data is then transmitted to a computer via a network cable for subsequent processing.

[0135] The computer-readable storage medium of the present application embodiment stores a computer program thereon, which, when executed by a processor, implements the steps of the solder defect detection method of any of the above embodiments.

[0136] Computer programs can be stored in memory. Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the above method embodiments. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in memory, thereby implementing the methods in the above method embodiments.

[0137] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0138] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processing module, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0139] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0140] It should be understood that various parts of the embodiments of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0141] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0142] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0143] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0144] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for detecting solder defects, characterized in that, include: Obtain the original 3D image of the solder area; Convert the original 3D image into a 2D image; Process the 2D image to obtain the outer edge information of the solder area; Based on the outer edge information, the solder area is divided into different detection zones in the original 3D image; Determine whether solder defects exist within the detection area; If a solder defect exists in the detection area, determine the information of the solder defect; Determining whether solder defects exist within the detection area includes: Based on the detection area, a new 3D image of the solder area is re-acquired; The new 3D image is compared with the original 3D image to highlight the image of the detection area; The presence of solder defects is determined based on the image of the highlighted detection area.

2. The detection method according to claim 1, characterized in that, The process of converting the original 3D image into a 2D image includes: According to the grayscale mapping relationship, the 3D image with a first predetermined number of bits is converted into the 2D image with a second predetermined number of bits, wherein the first predetermined number of bits is greater than the second predetermined number of bits.

3. The detection method according to claim 1, characterized in that, The process of processing the 2D image to obtain the outer edge information of the solder area includes: The 2D image is processed using a predetermined method to obtain a clear solder area image, the predetermined processing method including at least one of contrast enhancement, binarization, and morphological processing; Perform a Blob operation on the solder area image to obtain the outer edge information of the solder area.

4. The detection method according to claim 1, characterized in that, Before comparing the new 3D image with the original 3D image, determining whether solder defects exist within the detection area further includes: Interpolate the coordinates of the new 3D image to the positions in the original 3D image where there is no data; The center of the new 3D image is completely aligned with the center of the original 3D image.

5. The detection method according to claim 1, characterized in that, The detection method further includes: Upload the information about the solder defect.

6. The detection method according to claim 1, characterized in that, The detection method further includes: Establish a communication connection with the 3D scanning camera; Control the 3D scanning camera to operate so that the 3D scanning camera can acquire the original 3D image.

7. A detection device, characterized in that, include: The acquisition module is used to acquire the original 3D image of the solder area; A conversion module is used to convert the original 3D image into a 2D image; The processing module is used to process the 2D image to obtain the outer edge information of the solder area; The segmentation module, based on the outer edge information, divides the solder area into different detection zones in the original 3D image; The first determining module determines whether solder defects exist in the detection area; The second determining module determines information about the solder defect if a solder defect exists in the detection area. Determining whether solder defects exist within the detection area includes: Based on the detection area, a new 3D image of the solder area is re-acquired; The new 3D image is compared with the original 3D image to highlight the image of the detection area; The presence of solder defects is determined based on the image of the highlighted detection area.

8. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the detection method as described in any one of claims 1-6.

9. A non-volatile computer-readable storage medium with computer-executable instructions, characterized in that, When the computer-executable instructions are executed by one or more processors, the processors perform the detection method according to any one of claims 1-6.

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