Washing machine transportation bolt detection method, device, equipment, medium and program product

Abstract

The invention relates to a washing machine transportation bolt detection method, device and equipment, a medium and a program product. The method comprises the following steps: acquiring a to-be-recognized image; and according to the to-be-identified image, generating a detection result including the transport bolt dismounting condition of the washing machine in the to-be-identified image, the transport bolt dismounting condition being determined based on the information of the target back area of the washing machine in the to-be-identified image. Thus, by obtaining the to-be-recognized image and generating the detection result based on the target back area information of the washing machine, the dismounting condition of the transportation bolt can be rapidly and accurately judged, errors caused by manual detection are reduced, the detection efficiency and accuracy are improved, and then normal operation of the washing machine is guaranteed.

Description

Technical Field

[0001] This disclosure relates to the field of image processing, and more particularly to a method, apparatus, equipment, medium, and program product for detecting transport bolts of a washing machine. Background Technology

[0002] Before leaving the factory, drum washing machines use transport bolts to secure the inner drum to prevent damage during transportation. These bolts must be removed during on-site installation to restore the drum to normal operation. If the bolts are not removed, the washing machine will experience severe vibration and displacement during spin-drying and other operations, affecting the user experience and potentially causing equipment damage, leading to after-sales issues. Currently, verification of this removal process mainly relies on manual inspection of installation photos, a method that is inefficient, subjective, and lacks effective data traceability. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides a method, apparatus, equipment, medium, and procedure for detecting transport bolts of a washing machine.

[0004] According to a first aspect of the present disclosure, a method for detecting transport bolts of a washing machine is provided, comprising: Acquire the image to be recognized; Based on the image to be identified, a detection result is generated including the removal status of the transport bolts of the washing machine in the image to be identified, wherein the removal status of the transport bolts is determined based on information of the target back area of ​​the washing machine in the image to be identified.

[0005] In the above technical solution, by acquiring the image to be identified and generating detection results based on the information of the back area of ​​the washing machine, the removal status of the transport bolts can be quickly and accurately determined, reducing errors caused by manual inspection, improving detection efficiency and accuracy, and thus ensuring the normal operation of the washing machine.

[0006] In some possible implementations, generating a detection result based on the image to be identified, including the removal status of the transport bolts of the washing machine in the image to be identified, includes: Detect the presence of the target's back region in the image to be identified; The detection method identifies the removal status of the washing machine's transport bolts in the image to be identified, where the target's back region is present. The detection results are generated.

[0007] In the above technical solution, the target's back area and the removal status of transport bolts are detected step by step. Images that do not contain valid detection targets can be quickly filtered out at the front end, avoiding the waste of computing resources caused by subsequent bolt detection on invalid images, and improving the reliability and output efficiency of detection results.

[0008] In some possible implementations, detecting the presence of the target's back region in the image to be identified includes: The image to be identified is identified by a pre-trained classification model to determine the presence of the target back region in the image to be identified; The classification model is trained using a first sample image and the annotation information of the target back region of the washing machine in the first sample image.

[0009] In the above technical solution, a classification model that has been pre-trained and trained based on the first sample image and annotation information can accurately identify whether the target back area of ​​the washing machine exists in the image to be identified.

[0010] In some possible implementations, detecting the removal of transport bolts from the washing machine in the image to be identified, where the target's back region is located, includes: The image to be identified is used to identify the back region of the target by a pre-trained detection model; Based on the number of transport bolts identified by the detection model, the removal status of the transport bolts is determined; The detection model is trained using the second sample image and the annotation information of the washing machine transport bolts in the second sample image.

[0011] In the above technical solution, the detection model, which is pre-trained and based on the second sample image and annotation information, identifies the image and determines the dismantling status based on the number of identified transport bolts. This can accurately identify the bolts and make the judgment of the dismantling status more reliable.

[0012] In some possible implementations, the identification of the image containing the target's back region using a pre-trained detection model includes: The detection model is used to generate a detection box in the image to be identified, and the detection box is used to locate potential washing machine transport bolts; For each detection box, determine the category and confidence level corresponding to that detection box; The number of detection boxes whose output category is bolt and whose confidence level exceeds a set threshold is determined as the number of transport bolts.

[0013] In the above technical solution, by generating detection boxes and combining them with category and confidence threshold for filtering, the accuracy of quantity statistics can be ensured, the probability of false detection and false negative detection can be reduced, and the detection results can be made more reliable.

[0014] In some possible implementations, determining the removal status of the transport bolts based on the number of transport bolts identified by the detection model includes: If the number of transport bolts is less than the preset value, it is determined that the transport bolts have been removed; If the number of transport bolts is greater than or equal to the preset value, it is determined that the transport bolts have not been removed.

[0015] In the above technical solution, by simply comparing the number of identified bolts with a preset value, the judgment result of the bolt removal status can be given quickly and accurately.

[0016] In some possible implementations, the image to be identified is an image uploaded in real time, or an image extracted from a target database based on a preset time period.

[0017] In the above technical solution, the source of the image to be identified is flexible. It can be uploaded in real time to meet the needs of immediate detection, or it can be extracted from the target database according to a preset time period, adapting to different scenarios.

[0018] In some possible implementations, the method further includes: When the classification model fails to detect the target back area of ​​the washing machine, the image to be identified is marked as an invalid image.

[0019] The above technical solution can quickly filter out invalid data, reduce the workload of subsequent processing, improve the overall detection efficiency, and concentrate resources on the detection of valid images.

[0020] In some possible implementations, the method further includes: Determine the hash value of each image to be identified; Add duplicate tags to images to be identified that have duplicate hash values.

[0021] In the above technical solution, determining the hash value of the image to be identified and adding a duplicate mark to duplicate images can effectively identify duplicate images, avoid repeated processing of the same images, save computing resources, and improve the efficiency of the detection process.

[0022] In some possible implementations, the method further includes: For multiple images to be identified that have duplicate hash values, the same identification result is reused.

[0023] The above technical solution can avoid the waste of computing resources caused by repeatedly recognizing the same image, improve image recognition efficiency, and shorten the overall processing time.

[0024] In some possible implementations, the detection result may also include a hash value, work order number, and image link address corresponding to the image to be identified.

[0025] The above technical solution facilitates the traceability, management, and querying of test results, providing detailed and accurate information for subsequent equipment maintenance and troubleshooting.

[0026] In some possible implementations, the target back area of ​​the washing machine is the entire back area of ​​the washing machine.

[0027] The above technical solution can avoid missing the detection of transport bolts due to incomplete images, and ensure the accuracy of the detection results by ensuring the integrity of the back area of ​​the washing machine in the image to be identified.

[0028] According to a second aspect of the present disclosure, a washing machine transport bolt detection device is provided, comprising: The acquisition module is used to acquire the image to be recognized; The generation module is used to generate a detection result based on the image to be identified, including the removal status of the transport bolts of the washing machine in the image to be identified, wherein the removal status of the transport bolts is determined based on information of the target back area of ​​the washing machine in the image to be identified.

[0029] According to a third aspect of the present disclosure, an electronic device is provided, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to execute the executable instructions in the memory to implement the steps of the washing machine transport bolt detection method provided in the first aspect of this disclosure.

[0030] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the washing machine transport bolt detection method provided in the first aspect of the present disclosure.

[0031] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the washing machine transport bolt detection method provided in the first aspect of the present disclosure.

[0032] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0033] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0034] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0035] Figure 1This is a flowchart illustrating a method for detecting transport bolts of a washing machine according to an exemplary embodiment.

[0036] Figure 2 This is a flowchart illustrating a method for detecting transport bolts of a washing machine according to an exemplary embodiment.

[0037] Figure 3 This is a flowchart illustrating a method for detecting transport bolts of a washing machine according to an exemplary embodiment.

[0038] Figure 4 This is a block diagram illustrating a washing machine transport bolt detection device according to an exemplary embodiment.

[0039] Figure 5 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0040] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0041] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.

[0042] Figure 1 This is a flowchart illustrating a method for detecting transport bolts of a washing machine according to an exemplary embodiment. Figure 1 As shown, the method may include steps S101 to S102.

[0043] In step S101, the image to be identified is acquired.

[0044] In one embodiment, an image acquisition device can be used to capture an image to be identified. For ease of traceability, the image to be identified can be associated with information such as a corresponding work order number.

[0045] In step S102, a detection result is generated based on the image to be identified, including the removal status of the transport bolts of the washing machine in the image to be identified.

[0046] The removal status of the transport bolts is determined based on information from the target back area of ​​the washing machine in the image to be identified.

[0047] In one embodiment, a deep learning-based model can be used to identify the image to be recognized and generate detection results. For example, the model can be trained on a large number of images that have been labeled with the target back area of ​​the washing machine and the transport bolts, enabling it to accurately locate and identify the transport bolts within the target back area. The model can then use the determined number of transport bolts to determine the removal status of the washing machine's transport bolts. Thus, using a model for image recognition can improve the accuracy and efficiency of the detection results, achieving automation of the detection process.

[0048] In the above technical solution, by acquiring the image to be identified and generating detection results based on the information of the back area of ​​the washing machine, the removal status of the transport bolts can be quickly and accurately determined, reducing errors caused by manual inspection, improving detection efficiency and accuracy, and thus ensuring the normal operation of the washing machine.

[0049] In some possible implementations, the target back area of ​​the washing machine is the entire back area of ​​the washing machine. This avoids missing transport bolts due to incomplete images, and ensures the accuracy of the detection results by guaranteeing the integrity of the back area of ​​the washing machine in the image to be identified.

[0050] In some possible implementations, the image to be identified is an image uploaded in real time.

[0051] For example, in the installation of a washing machine, after installation, the installer can use a mobile device to take a real-time image of the back of the washing machine and upload it to the detection system. The detection system can automatically identify the uploaded image in real time to determine whether the transport bolts of the washing machine have been completely removed. Once the presence of the transport bolts is detected, the detection system can trigger an early warning mechanism to notify relevant personnel to handle the situation promptly. This avoids problems such as drastic displacement, noise, and damage during the operation of the washing machine, thereby reducing user complaints, lowering after-sales processing costs, and achieving real-time monitoring of installation quality.

[0052] In some possible implementations, the image to be identified is an image extracted from a target database based on a preset time period.

[0053] For example, the preset time period can be determined based on a set cycle, and the target database can be pre-defined. In scenarios involving regular quality inspections, a batch of installation images can be extracted from the target database at a set cycle (such as daily or weekly) as images to be identified for centralized automated inspection. This allows for rapid centralized quality inspection or automated review of large batches of installation images, generating traceable inspection results. This enables continuous monitoring and statistical analysis of installation quality. By analyzing the installation quality levels of different regions and service providers, it provides a basis for subsequent product quality improvement and installation specification development.

[0054] In the above technical solution, the image to be identified is an image uploaded in real time or an image extracted from the target database based on a preset time period. This enhances the flexibility and applicability of the washing machine transport bolt detection method provided by this disclosure, which can meet the needs of real-time monitoring, process historical data, and adapt to different scenarios.

[0055] Figure 2 This is a flowchart illustrating a method for detecting transport bolts of a washing machine according to an exemplary embodiment. Figure 2 As shown, step S102 includes steps S1021 to S1023.

[0056] In step S1021, the presence of the target back area of ​​the washing machine in the image to be identified is detected.

[0057] In one embodiment, the presence of the target back region of the washing machine in the image to be identified can be detected by the following method: The presence of the target back region of the washing machine in the image is determined by identifying the image using a pre-trained classification model.

[0058] The classification model can be trained using a first sample image and the annotation information of the target back region of the washing machine in the first sample image. In this way, the trained classification model can effectively identify the target back region of the washing machine.

[0059] For example, if the classification model outputs a predicted category of "back of a washing machine" and the corresponding confidence level exceeds a preset confidence threshold, then the image to be identified is determined to contain the target back panel region of a washing machine. Thus, by combining this with the preset confidence threshold, the accuracy and robustness of washing machine back panel region detection can be improved.

[0060] For example, the image to be recognized can be input into a classification model. :

[0061] in, For the k-th image to be identified; For classification models The recognition result for whether the k-th image to be recognized includes the target back region of the washing machine. If the image to be identified is invalid (the target back panel area of ​​the washing machine does not exist), it can be output as the detection result; if If the target back panel area of ​​the washing machine exists, then step S1022 can be executed to detect the transport bolts.

[0062] In one implementation, if the classification model fails to detect the target back area of ​​the washing machine, the image to be identified is marked as an invalid image. This avoids subsequent invalid processing, saves computational resources, and helps users quickly identify problematic images, thereby improving overall detection efficiency and data quality.

[0063] In another implementation, the classification model can be replaced with a detection model or other models that can achieve the same function to detect the target back area of ​​the washing machine.

[0064] Furthermore, if the model used for target back region detection has region localization and extraction capabilities, it can accurately locate the target back region in the image to be identified and crop it from the original image to generate an independent back region image. Then, the cropped back region image is used to detect the removal of transport bolts. This effectively eliminates interference from complex backgrounds in the original image, improving the accuracy and reliability of the judgment regarding the removal of transport bolts.

[0065] In step S1022, the removal status of the transport bolts of the washing machine in the image to be identified, where the target back area exists, is detected.

[0066] In one embodiment, the removal of transport bolts from a washing machine in an image to be identified, where the target's back area is present, can be detected in the following manner: The image to be identified is identified by using a pre-trained detection model that contains the back region of the target. The number of transport bolts identified by the detection model determines the status of bolt removal.

[0067] The detection model can be trained using a second sample image and the annotation information of the washing machine transport bolts in the second sample image. In this way, the trained detection model can effectively identify the transport bolts in the image.

[0068] For example, an image containing a target's back region can be identified using a pre-trained detection model in the following way: A detection bounding box is generated in the image to be identified using a detection model. The detection bounding box is used to locate potential washing machine transport bolts. For each detection box, determine the corresponding category and confidence level; The number of detection boxes whose output category is bolt and whose confidence level exceeds the set threshold is determined as the number of transport bolts.

[0069] For example, an image of the target with a back region can be input into the detection model. :

[0070] in, Let K be the set of detection boxes for the k-th image to be identified. Each detection box... There are corresponding output categories and confidence level The number of transport bolts can be determined using the following formula. :

[0071] in, This is an indicator function. Using this formula, detection boxes generated by the detection model that output the category "bolt" and have a confidence level exceeding a set threshold (e.g., 0.8) can be selected. The number of selected detection boxes is then determined as the number of transport bolts. In this way, by using a pre-trained detection model to identify transport bolts, combined with the set threshold corresponding to the confidence level, the accuracy and robustness of the transport bolt detection results can be improved.

[0072] For example, the removal status of transport bolts can be determined based on the number of transport bolts identified by the detection model in the following way: If the number of transport bolts is less than the preset value, it is determined that the transport bolts have been removed; If the number of transport bolts is greater than or equal to the preset value, it is determined that the transport bolts have not been removed.

[0073] The preset value can be set based on actual needs; for example, it can be set to 1. The removal status of the transport bolts can be determined using the following formula. :

[0074] If the classification model fails to detect the target back area of ​​the washing machine, it can... The value is set to 0 to indicate that the image to be identified is an invalid image.

[0075] In this way, by simply comparing the number of identified bolts with the preset value, the judgment result of the bolt removal status can be given quickly and accurately.

[0076] In step S1023, the detection result is generated.

[0077] In one embodiment, in addition to the removal status of the transport bolts of the washing machine in the image to be identified, the detection result may also include the hash value, work order number, and image link address corresponding to the image to be identified.

[0078] The detection results can be output in a structured format, for example, detection results .in, Let k be the work order number of the k-th image to be recognized. Let k be the image link address of the image to be identified. Let k be the hash value of the image to be identified. This represents the removal status of transport bolts on the washing machine in the k-th image to be identified. The detection results can be saved as a structured Parquet file and can also be uploaded to a quality control platform for early warning and traceability.

[0079] Furthermore, graphical icons (such as warnings) can be added to the test results for use by the quality inspection system or report display. For example, a rocket icon indicates that the bolt has been removed, a warning icon indicates that the bolt was not removed, and a cross indicates that the image is unqualified, so that quality inspectors or business systems can quickly understand the results.

[0080] exist Figure 2 The provided technical solution, by confirming the existence of the target back area of ​​the washing machine, can quickly filter out images that do not contain valid detection targets (such as photos of other parts mistakenly transmitted), avoiding the waste of computational resources caused by performing subsequent transport bolt detection on invalid images. Only images confirming the existence of the target back area are analyzed for transport bolt removal, ensuring the targeting and accuracy of subsequent detection. Thus, by detecting the target back area and transport bolt removal step by step, the reliability of the detection results and the output efficiency can be improved.

[0081] In some possible implementations, the washing machine transport bolt detection method provided in this disclosure may further include: Determine the hash value of each image to be identified; Add duplicate tags to images to be identified that have duplicate hash values.

[0082] In one embodiment, a hash value algorithm from related technologies can be used to generate a unique digital fingerprint (i.e., hash value) for each image to be identified. Different images to be identified correspond to different hash values. Before identifying an image, its hash value can be determined and compared with existing records in the database. If a duplicate hash value is found, it indicates that the image content is completely consistent with historical images, and a duplicate marker can be added. By adding duplicate markers, the behavior of relevant personnel repeatedly uploading the same image can be effectively identified. This avoids the waste of resources caused by repeated calculations and provides a basis for the standardized supervision of installation operations.

[0083] In some possible implementations, the washing machine transport bolt detection method provided in this disclosure may further include: For multiple images to be identified that have duplicate hash values, the same identification result is reused.

[0084] For example, for images marked as duplicates, further recognition and analysis can be avoided. Instead, the previously generated and cached detection results can be retrieved directly using their hash values ​​as keys. This avoids wasting computational resources due to repeatedly recognizing the same images, improves image recognition efficiency, and shortens the overall processing time.

[0085] Figure 3 This is a flowchart illustrating a method for detecting transport bolts of a washing machine according to an exemplary embodiment. Through this... Figure 3 This allows for a clearer understanding of the implementation process of the washing machine transport bolt detection method provided in this disclosure.

[0086] like Figure 3 As shown, the image link address of the image to be identified can be queried from the target database. The image to be identified is then downloaded based on this link address, and a hash value is generated. A classification model is used to identify whether the target back region of the washing machine exists in the image to be identified. If it does not exist, the image to be identified is marked as invalid. If it exists, a detection model is used to determine whether the transport bolts of the washing machine in the image to be identified have been removed. If the detection model outputs that the bolts have not been removed or have been removed, a structured result can then be generated and output based on the model's output.

[0087] In this way, by downloading images through query links and generating hash values, and using a two-level model to sequentially determine the target's back area and the removal status of transport bolts, image analysis can be achieved efficiently and accurately. This reduces interference from invalid images, accurately outputs structured results, provides a reliable basis for subsequent quality inspection and maintenance, and improves overall work efficiency and quality.

[0088] Figure 4 This is a block diagram illustrating a washing machine transport bolt detection device 300 according to an exemplary embodiment. Figure 4 As shown, the washing machine transport bolt detection device 300 includes: The acquisition module 301 is used to acquire the image to be recognized; The generation module 302 is used to generate a detection result including the removal status of the transport bolts of the washing machine in the image to be identified, wherein the removal status of the transport bolts is determined based on information of the target back area of ​​the washing machine in the image to be identified.

[0089] In the above technical solution, by acquiring the image to be identified and generating detection results based on the information of the back area of ​​the washing machine, the removal status of the transport bolts can be quickly and accurately determined, reducing errors caused by manual inspection, improving detection efficiency and accuracy, and thus ensuring the normal operation of the washing machine.

[0090] In some possible implementations, the generation module 302 includes: The first detection submodule is used to detect the presence of the target back region in the image to be identified; The second detection submodule is used to detect the removal status of the washing machine's transport bolts in the image to be identified, where the target's back area exists. A generation submodule is used to generate the detection results.

[0091] In some possible implementations, the first detection submodule is used to detect the presence of the target back region in the image to be identified by: The image to be identified is identified by a pre-trained classification model to determine the presence of the target back region in the image to be identified; The classification model is trained using a first sample image and the annotation information of the target back region of the washing machine in the first sample image.

[0092] In some possible implementations, the second detection submodule is used to detect the removal of shipping bolts from a washing machine in an image to be identified where the target's back region is located, by: The image to be identified is used to identify the back region of the target by a pre-trained detection model; Based on the number of transport bolts identified by the detection model, the removal status of the transport bolts is determined; The detection model is trained using the second sample image and the annotation information of the washing machine transport bolts in the second sample image.

[0093] In some possible implementations, the second detection submodule is used to identify the image containing the target's back region using a pre-trained detection model in the following manner: The detection model is used to generate a detection box in the image to be identified, and the detection box is used to locate potential washing machine transport bolts; For each detection box, determine the category and confidence level corresponding to that detection box; The number of detection boxes whose output category is bolt and whose confidence level exceeds a set threshold is determined as the number of transport bolts.

[0094] In some possible implementations, the second detection submodule is used to determine the removal status of the transport bolts based on the number of transport bolts identified by the detection model, including: If the number of transport bolts is less than the preset value, it is determined that the transport bolts have been removed; If the number of transport bolts is greater than or equal to the preset value, it is determined that the transport bolts have not been removed.

[0095] In some possible implementations, the image to be identified is an image uploaded in real time, or an image extracted from a target database based on a preset time period.

[0096] In some possible implementations, the first detection submodule is further configured to mark the image to be identified as an invalid image when the classification model does not detect the target back area of ​​the washing machine.

[0097] In some possible implementations, the washing machine transport bolt detection device 300 further includes: The determination module is used to determine the hash value of each image to be identified; The duplicate tagging module is used to add duplicate tags to images to be identified that have duplicate hash values.

[0098] In some possible implementations, the washing machine transport bolt detection device 300 further includes: The reuse module is used to reuse the same recognition result for multiple images to be recognized that have duplicate hash values.

[0099] In some possible implementations, the detection result may also include a hash value, work order number, and image link address corresponding to the image to be identified.

[0100] In some possible implementations, the target back area of ​​the washing machine is the entire back area of ​​the washing machine.

[0101] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0102] Figure 5 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. For example, the electronic device 1900 may be provided as a server. (Refer to...) Figure 5 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the aforementioned washing machine transport bolt detection method.

[0103] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output interface 1958. Electronic device 1900 can operate on an operating system, such as Windows Server, stored in memory 1932. TM Mac OS X TM Unix TM Linux TM FreeBSD TM Or similar.

[0104] In another exemplary embodiment, this disclosure also provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the steps of the washing machine transport bolt detection method provided in this disclosure.

[0105] In another exemplary embodiment, this disclosure also provides a computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the above-described washing machine transport bolt detection method when executed by the programmable device.

[0106] Those skilled in the art will also understand that the various illustrative logical blocks and steps listed in the embodiments of this application can be implemented by electronic hardware, computer software, or a combination of both. Whether such functionality is implemented through hardware or software depends on the specific application and the overall system design requirements. Those skilled in the art can implement the described functionality using various methods for each specific application, but such implementation should not be construed as exceeding the scope of protection of the embodiments of this application.

[0107] It should be understood that, unless otherwise specifically indicated, features of various embodiments of this disclosure described herein can be combined with each other. As used herein, the term “and / or” includes any one of the relevant listed items and any combination of any two or more; similarly, “at least one of…” includes any one of the relevant listed items and any combination of any two or more.

[0108] Although terms such as “first,” “second,” and “third” may be used herein to describe various components, parts, regions, layers, or sections, these components, parts, regions, layers, or sections are not limited to these terms. Rather, these terms are used only to distinguish one component, part, region, layer, or section from another. Therefore, without departing from the teachings of the examples described herein, the first component, part, region, layer, or section mentioned in the examples may also be referred to as the second component, part, region, layer, or section. Furthermore, 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 at least one of that feature. In the description herein, “a plurality” means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0109] Furthermore, the term “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as advantageous compared to other aspects or designs. Rather, the use of the term “exemplary” is intended to present the concept in a concrete manner. As used herein, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise specified or clear from the context, “X applies A or B” is intended to mean any of the natural inclusive arrangements. That is, “X applies A or B” satisfies any of the foregoing instances if X applies A; X applies B; or both X applies A and B. Additionally, unless otherwise specified or clear from the context to refer to the singular form, the articles “a” and “an” as used in this application and the appended claims are generally understood to mean “one or more.”

[0110] Similarly, although this disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art upon reading and understanding this specification and the accompanying drawings. This disclosure includes all such modifications and variations and is limited only by the scope of the claims. In particular, with respect to the various functions performed by the components described above (e.g., elements, resources, etc.), unless otherwise indicated, the terminology used to describe such components is intended to correspond to any component (functionally equivalent) that performs the specific function of the described component, even if structurally not equivalent to the disclosed structure. Furthermore, although specific features of this disclosure may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of other implementations, as may be desired and advantageous to any given or particular application. Moreover, with regard to the terms “comprising,” “owning,” “having,” “having,” or variations thereof as used in the detailed description or claims, such terms are intended to be inclusive in a manner similar to the term “including.”

[0111] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

[0112] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for detecting bolts used in the transport of a washing machine, characterized in that, include: Acquire the image to be recognized; Based on the image to be identified, a detection result is generated including the removal status of the transport bolts of the washing machine in the image to be identified, wherein the removal status of the transport bolts is determined based on information of the target back area of ​​the washing machine in the image to be identified.

2. The method according to claim 1, characterized in that, The step of generating a detection result based on the image to be identified, including the removal status of the transport bolts of the washing machine in the image to be identified, includes: Detect the presence of the target's back region in the image to be identified; The detection method identifies the removal status of the washing machine's transport bolts in the image to be identified, where the target's back region is present. The detection results are generated.

3. The method according to claim 2, characterized in that, The detection of the presence of the target back region in the image to be identified includes: The image to be identified is identified by a pre-trained classification model to determine the presence of the target back region in the image to be identified; The classification model is trained using a first sample image and the annotation information of the target back region of the washing machine in the first sample image.

4. The method according to claim 2, characterized in that, The detection of the removal status of the washing machine's transport bolts in the image to be identified, which exists in the target's back region, includes: The image to be identified is used to identify the back region of the target by a pre-trained detection model; Based on the number of transport bolts identified by the detection model, the removal status of the transport bolts is determined; The detection model is trained using the second sample image and the annotation information of the washing machine transport bolts in the second sample image.

5. The method according to claim 4, characterized in that, The process of identifying the image containing the target's back region using a pre-trained detection model includes: The detection model is used to generate a detection box in the image to be identified, and the detection box is used to locate potential washing machine transport bolts; For each detection box, determine the category and confidence level corresponding to that detection box; The number of detection boxes whose output category is bolt and whose confidence level exceeds a set threshold is determined as the number of transport bolts.

6. The method according to claim 4, characterized in that, The step of determining the removal status of the transport bolts based on the number of transport bolts identified by the detection model includes: If the number of transport bolts is less than the preset value, it is determined that the transport bolts have been removed; If the number of transport bolts is greater than or equal to the preset value, it is determined that the transport bolts have not been removed.

7. The method according to claim 1, characterized in that, The image to be identified is either an image uploaded in real time or an image extracted from a target database based on a preset time period.

8. The method according to claim 3, characterized in that, The method further includes: When the classification model fails to detect the target back area of ​​the washing machine, the image to be identified is marked as an invalid image.

9. The method according to claim 1, characterized in that, The method further includes: Determine the hash value of each image to be identified; Add duplicate tags to images to be identified that have duplicate hash values.

10. The method according to claim 9, characterized in that, The method further includes: For multiple images to be identified that have duplicate hash values, the same identification result is reused.

11. The method according to claim 1, characterized in that, The detection results also include the hash value, work order number, and image link address corresponding to the image to be identified.

12. The method according to any one of claims 1-11, characterized in that, The target back area of ​​the washing machine is the entire back area of ​​the washing machine.

13. A device for detecting bolts used in the transport of a washing machine, characterized in that, include: The acquisition module is used to acquire the image to be recognized; The generation module is used to generate a detection result based on the image to be identified, including the removal status of the transport bolts of the washing machine in the image to be identified, wherein the removal status of the transport bolts is determined based on information of the target back area of ​​the washing machine in the image to be identified.

14. The apparatus according to claim 13, characterized in that, The generation module includes: The first detection submodule is used to detect the presence of the target back region in the image to be identified; The second detection submodule is used to detect the removal status of the washing machine's transport bolts in the image to be identified, where the target's back area exists. A generation submodule is used to generate the detection results.

15. The apparatus according to claim 14, characterized in that, The first detection submodule is used to detect the presence of the target back region in the image to be identified in the following manner: The image to be identified is identified by a pre-trained classification model to determine the presence of the target back region in the image to be identified; The classification model is trained using a first sample image and the annotation information of the target back region of the washing machine in the first sample image.

16. The apparatus according to claim 14, characterized in that, The second detection submodule is used to detect the removal status of the washing machine's transport bolts in the image to be identified, where the target's back region is located, in the following manner: The image to be identified is used to identify the back region of the target by a pre-trained detection model; Based on the number of transport bolts identified by the detection model, the removal status of the transport bolts is determined; The detection model is trained using the second sample image and the annotation information of the washing machine transport bolts in the second sample image.

17. The apparatus according to claim 13, characterized in that, The image to be identified is either an image uploaded in real time or an image extracted from a target database based on a preset time period.

18. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to execute the executable instructions in the memory to implement the steps of the washing machine transport bolt detection method according to any one of claims 1-12.

19. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the washing machine transport bolt detection method according to any one of claims 1-12.

20. A computer program product, characterized in that, The method includes a computer program that, when executed by a processor, implements the steps of the washing machine transport bolt detection method according to any one of claims 1-12.

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