Detection method, mobile terminal, electronic equipment and storage medium

Abstract

The invention provides a detection method, a mobile terminal, electronic equipment and a storage medium. The detection method is applied to the mobile terminal, and the mobile terminal is provided with a data interface used for communicating with a back splint of the mobile terminal. The detection method comprises the steps of determining an artificial intelligence processing service needing to be executed for a target object; according to the artificial intelligence processing service, collecting a to-be-detected image of the target object; transmitting information about the artificial intelligence processing service and the to-be-detected image to the mobile terminal back splint, so that the mobile terminal back splint detects the to-be-detected image by using an artificial intelligence model corresponding to the artificial intelligence processing service; and receiving and storing a detection result of the to-be-detected image from the mobile terminal back splint. According to the scheme, the mobile terminal can be utilized to detect the detected object.

Description

Technical Field

[0001] This invention relates to the field of intelligent detection technology, specifically to a detection method, a mobile terminal, an electronic device, a storage medium, and a computer program product. Background Technology

[0002] With the development of technology, the detection of target objects is being applied to more and more application scenarios. For example, images of electricity meters can be acquired, and target detection can be performed on these images to detect defects in the meters.

[0003] In some scenarios, target detection is achieved using a dedicated mobile terminal. After acquiring an image of the target object, an AI model running on the mobile terminal can perform corresponding AI processing (such as target detection) to analyze the image and obtain relevant information about the target object. However, this dedicated mobile terminal not only needs to acquire images but also needs to perform target detection, so it is usually quite large. In the aforementioned scenarios, this dedicated mobile terminal needs to be carried on-site, causing inconvenience to the user. Moreover, because this dedicated mobile terminal combines multiple functions such as image acquisition and target detection, it is expensive, resulting in high hardware costs. Summary of the Invention

[0004] The present invention was proposed in view of the above-mentioned problems.

[0005] According to a first aspect of the present invention, a detection method is provided for a mobile terminal, the mobile terminal being provided with a data interface for communicating with a mobile terminal back clip. The method includes: determining an artificial intelligence processing service to be performed on a target object; acquiring an image of the target object to be detected according to the artificial intelligence processing service; transmitting information about the artificial intelligence processing service and the image to be detected to the mobile terminal back clip, so that the mobile terminal back clip detects the image to be detected using an artificial intelligence model corresponding to the artificial intelligence processing service; and receiving and storing the detection result of the image to be detected from the mobile terminal back clip.

[0006] For example, acquiring the image to be detected of the target object according to the artificial intelligence processing service includes: when the image acquisition function of the mobile terminal is enabled, performing an image acquisition operation at a preset frequency according to the artificial intelligence processing service to acquire the image to be detected.

[0007] For example, the method further includes: generating and displaying a detected image containing target information about the detection result based on the image to be detected and the detection result.

[0008] For example, the detection result includes at least one of the following: the location information of the target object in the detected image, whether the target object has defects, and the defect information of the target object.

[0009] For example, the location information is identified in the detected image in the form of a detection box, and / or the defect information of the target object is identified in the detected image in the form of text or pattern.

[0010] For example, the mobile terminal back clip is provided with a radio frequency identification (RFID) module, and the method further includes: receiving at least one RFID tag code of an object to be inspected from the mobile terminal back clip, wherein the RFID module is used to identify the RFID tag code of the object to be inspected based on RFID technology; and determining the target object among the at least one object to be inspected based on the received RFID tag code.

[0011] For example, determining the target object among the at least one object to be inspected based on the received radio frequency identification code includes: for each object to be inspected, determining whether to perform the artificial intelligence processing service on that object, and determining the object to be inspected for which the artificial intelligence processing service is performed as the target object, wherein the determined target object is multiple target objects;

[0012] The step of acquiring the image to be detected of the target object according to the artificial intelligence processing service includes: acquiring the images to be detected of the plurality of target objects sequentially according to the artificial intelligence processing service.

[0013] For example, the method further includes: determining a non-AI processing service performed by the mobile terminal back clip on the target object; receiving the processing result of the non-AI processing service from the mobile terminal back clip; wherein the detection of the image to be detected is performed after the completion of the non-AI processing service.

[0014] For example, the mobile terminal back clip is equipped with an infrared recognition module. The non-AI processing service includes reading and writing services for the target object using the infrared recognition module of the mobile terminal back clip. Receiving the processing result of the non-AI processing service from the mobile terminal back clip includes: receiving the processing result from the mobile terminal back clip in a preset communication mode. The processing result includes target information of the target object, wherein the target information is read and written by the mobile terminal back clip by executing the read and write service.

[0015] According to a second aspect of the present invention, a mobile terminal is also provided, the mobile terminal being provided with a data interface for communicating with a mobile terminal back clip, the mobile terminal being used to perform the detection method described above.

[0016] According to a third aspect of the present invention, an electronic device is also provided, comprising: a processor and a memory, wherein the memory stores computer program instructions, which are executed by the processor to perform the detection method described above.

[0017] According to a fourth aspect of the present invention, a storage medium is also provided, on which program instructions are stored, which, when executed, are used to perform the detection method described above.

[0018] According to a fifth aspect of the present invention, a computer program product is also provided, comprising computer program instructions that, when executed, perform the detection method described above.

[0019] In the above technical solution, a mobile terminal with a data interface for communication with a mobile terminal back clip is used to determine the artificial intelligence processing service to be performed on the target object. Based on the artificial intelligence processing service, an image of the target object to be detected is acquired. Then, the image to be detected and information about the artificial intelligence processing service are transmitted to the mobile terminal back clip, allowing the mobile terminal back clip to use an artificial intelligence model corresponding to the artificial intelligence processing service to detect the image to be detected. The detection result of the image to be detected is then received from and saved by the mobile terminal back clip. In this way, the mobile terminal does not need to have its own artificial intelligence model to obtain the detection result of the target object's image, reducing the performance requirements and hardware costs of the mobile terminal. Furthermore, the direct communication between the mobile terminal and the mobile terminal back clip via the data interface allows the mobile terminal to obtain the detection result of the image to be detected more quickly, avoiding external interference with data transmission between the mobile terminal and the mobile terminal back clip, thus making it more advantageous to use the mobile terminal for detection of the target object.

[0020] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0021] The above and other objects, features, and advantages of the present invention will become more apparent from the more detailed description of the embodiments of the invention in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same parts or steps.

[0022] Figure 1 A schematic flowchart of a detection method according to an embodiment of the present invention is shown;

[0023] Figure 2 A schematic diagram of a user interface related to artificial intelligence processing of electricity meters is shown according to an embodiment of the present invention.

[0024] Figure 3 A schematic diagram of a user interface for acquiring an image to be detected according to an embodiment of the present invention is shown;

[0025] Figure 4 A schematic diagram of a detected image is shown according to an embodiment of the present invention;

[0026] Figure 5 A schematic flowchart illustrating the determination of a target object among at least one object to be inspected, according to an embodiment of the present invention, is shown.

[0027] Figure 6 A schematic flowchart illustrating the receiving of processing results of non-artificial intelligence processing services according to an embodiment of the present invention is provided;

[0028] Figure 7 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of the present invention.

[0030] To at least partially address the aforementioned issues, a detection method is proposed. This method allows mobile terminals to acquire detection results of the target object's image without requiring their own artificial intelligence models, reducing the performance requirements and hardware costs of the mobile terminal. Furthermore, direct communication between the mobile terminal and its back clip via a data interface enables the mobile terminal to acquire detection results of the image more quickly, avoiding external interference with data transmission between the mobile terminal and its back clip. This makes it more advantageous to utilize the mobile terminal for object detection.

[0031] Figure 1 A schematic flowchart of a detection method according to an embodiment of the present invention is shown. Figure 1 As shown, this detection method is applied to a mobile terminal and may include steps S110 to S140. The mobile terminal is equipped with a data interface for communicating with a mobile terminal back clip, thereby enabling communication between the two devices. For example, the mobile terminal may be a mobile phone, tablet computer, or other similar device.

[0032] In step S110, the artificial intelligence processing tasks that need to be performed on the target object are determined.

[0033] The number of target objects can be one or multiple. The number of AI processing tasks can be related to the number of target objects. For example, the target objects can be divided into several parts, and then corresponding AI processing tasks can be determined for each part of the target objects.

[0034] The target object can be of one type or multiple types. The AI ​​processing tasks performed on the target object can be related to the type of the target object. For example, the target object can be divided into several parts based on its type. Then, for the type of the target object in each part, the corresponding AI processing tasks for each part can be determined. For example, when the target objects include electricity meters and people, the AI ​​processing tasks to be performed on the electricity meters and the AI ​​processing tasks to be performed on the people can be determined separately.

[0035] Multiple AI processing tasks that may need to be executed can be pre-configured and stored on the mobile terminal. After identifying the target object, the pre-defined AI processing tasks can be matched from this list. Alternatively, AI processing tasks for the target object can be received from a host computer or the cloud. The identified AI processing tasks can be one or more tasks, and each AI processing task can target one or more target objects.

[0036] Artificial intelligence (AI) processing services refer to processing operations implemented with the assistance of AI models. For example, the content of an AI processing service may include the target object, the AI ​​model used, the type of processing operations performed using the AI ​​model, and other related operations. For each AI service, the number and type of AI models used can be one or more, and the types of processing operations performed using the AI ​​models can also be one or more.

[0037] For example, the artificial intelligence model used in the artificial intelligence processing business can be a model for object detection, and the content of the artificial intelligence processing business can be to perform object detection on the target object in the image to be detected in order to obtain the corresponding detection results.

[0038] For example, in response to a user's selection or input command for artificial intelligence services, the artificial intelligence processing service to be performed on the target object can be directly determined by the selection or input operation.

[0039] For example, in response to a user's selection or input command regarding a target object, different AI processing tasks can be determined for that target object. Control components for these different AI processing tasks can then be displayed. Subsequently, in response to a user's selection or input command regarding the control components for the AI ​​processing tasks, the specific AI processing task to be performed on the target object can be determined.

[0040] For example, in response to a user's editing operation on an AI processing task for a target object, an AI processing task to be performed on the target object can be generated.

[0041] Figure 2 A schematic diagram of a user interface related to artificial intelligence processing of electricity meters is shown according to an embodiment of the present invention.

[0042] like Figure 2 As shown, taking an electricity meter as an example, users can select AI processing services for the electricity meter based on configurable options such as main body detection and defect detection in the user interface. Users can also enter the desired control component in the search box at the top of the user interface to display the corresponding control component. In addition to control components related to AI processing, the user interface can also include control components not related to AI processing.

[0043] In step S120, the image of the target object to be detected is acquired according to the artificial intelligence processing business.

[0044] After determining the AI ​​processing task, the mobile terminal camera lens can be pointed at the target object for which images need to be collected, and the image of the target object can be taken as the image to be detected.

[0045] For example, when the image acquisition function of the mobile terminal is enabled, the image acquisition operation can be performed at a preset frequency according to the artificial intelligence processing business to obtain the image to be detected.

[0046] The preset frequency can be determined from the established AI processing tasks, or it can be a pre-set image acquisition frequency. For example, the images acquired during the image acquisition operation can be directly used as the images to be detected.

[0047] Images acquired through image acquisition at a preset frequency can form continuous time-series data, facilitating subsequent analysis. Even if some acquired images are of poor quality or missing, images acquired at adjacent time points can still provide reference, ensuring that satisfactory images for detection are acquired each time. For example, images acquired through image acquisition can be combined with images from adjacent frames for correction or fusion, thereby improving image quality and obtaining high-quality images for detection.

[0048] The image acquisition function of a mobile terminal can be enabled by default. After determining the AI ​​processing task to be performed on the target object, the image of the target object to be detected can be acquired. Alternatively, the user can decide when to enable the image acquisition function, for example, by responding to the user's selection operation or input command on the control component of the image acquisition function, enabling or disabling the mobile terminal's image acquisition function.

[0049] By enabling the image acquisition function of the mobile terminal, it can be ensured that the mobile terminal can perform image acquisition operations normally. The images to be detected obtained by performing image acquisition operations at a preset frequency can form continuous time series data, which is convenient for subsequent analysis. Even if some images to be detected are of poor quality or are lost, images acquired at adjacent time points can still provide reference to obtain images to be detected that meet the requirements.

[0050] For example, when the image acquisition function of the mobile terminal is not enabled, the image of the target object to be detected may not be acquired, thus avoiding the acquisition of incorrect images.

[0051] Figure 3 A schematic diagram of a user interface for acquiring an image to be detected according to an embodiment of the present invention is shown.

[0052] like Figure 3 As shown, the user interface displays control components related to shooting parameters (the top icon), currently available images to be captured, preview windows of captured images to be captured, relevant prompts, and options to start shooting.

[0053] In step S130, information about the artificial intelligence processing service and the image to be detected are transmitted to the mobile terminal back clip, so that the mobile terminal back clip can use the artificial intelligence model corresponding to the artificial intelligence processing service to detect the image to be detected.

[0054] The information for AI processing tasks can include the execution sequence and content of the AI ​​processing tasks. A data interface communicating with the mobile terminal back clip can be used to transmit the AI ​​processing task information and the image to be detected from the mobile terminal to the mobile terminal back clip. The mobile terminal back clip can have at least one type of AI model pre-installed, and the back clip can use the AI ​​model corresponding to the AI ​​processing task to detect the image to obtain detection results. Understandably, because the AI ​​processing task is target-oriented, the AI ​​model of the mobile terminal back clip can detect the target object in the image to be detected, and the detection results are also related to the target object. For example, the AI ​​model of the mobile terminal back clip can also detect the image quality of the image to be detected.

[0055] For example, when there are multiple AI processing tasks, the mobile terminal back clip can be used to detect the image to be detected one by one using the AI ​​model corresponding to the AI ​​processing task, according to the execution order in the information of the AI ​​processing task, so as to obtain multiple detection results.

[0056] For example, the mobile terminal back clip can also be used to fuse multiple detection results of the same image to be detected into a single detection result.

[0057] In step S140, the detection result of the image to be detected is received from the back clip of the mobile terminal and saved.

[0058] For example, the detection result of the image to be detected can be an image for which the target object in the image to be detected has been identified.

[0059] For example, the detection result of the image to be detected can be the detection result for the target object only in the image to be detected. After obtaining the detection result of the image to be detected, the mobile terminal clip can send the detection result to the mobile terminal. The mobile terminal can perform operations such as stitching, overlaying and / or merging of the detection result and the corresponding image to be detected to obtain an image that identifies the target object in the image to be detected.

[0060] For example, after obtaining the detection result of the image to be detected, the mobile terminal back clip can store the detection result in a preset location. The mobile terminal can then read the detection result of the detected image from this preset location on the mobile terminal back clip.

[0061] For example, the detection results of an image to be detected may include whether a target object was detected, the location of the target object in the image, the size of the target object, and whether the shape of the target object has defects or anomalies. The specific type of detection result can be determined based on the actual AI processing business being performed and the AI ​​model actually used corresponding to the business objectives, which will not be detailed here.

[0062] In the above technical solution, a mobile terminal with a data interface for communication with a mobile terminal back clip is used to determine the artificial intelligence processing service to be performed on the target object. Based on the artificial intelligence processing service, an image of the target object to be detected is acquired. Then, the image to be detected and information about the artificial intelligence processing service are transmitted to the mobile terminal back clip. The mobile terminal back clip then uses the artificial intelligence model corresponding to the artificial intelligence processing service to detect the image to be detected. The detection result of the image to be detected is then received and saved from the mobile terminal back clip. In this way, the mobile terminal does not need to have its own artificial intelligence model to obtain the detection result of the target object's image, reducing the performance requirements and hardware costs of the mobile terminal. Furthermore, the direct communication between the mobile terminal and the mobile terminal back clip via the data interface allows the mobile terminal to obtain the detection result of the image to be detected more quickly, avoiding external interference with data transmission between the mobile terminal and the mobile terminal back clip, thus making it more advantageous to use the mobile terminal for detection of the target object.

[0063] For example, the detection method described above further includes step S150: generating and displaying a detected image containing target information about the detection results based on the image to be detected and the detection results.

[0064] The target information in the detection result is the identifier associated with the detection result. The detection result can include attribute information of the target object, such as its position, size, and morphological integrity in the image. A portion of this information can be selected to generate corresponding target information in the image to be detected, thus obtaining the detected image.

[0065] For example, the detection results include at least one of the following: the location information of the target object in the detected image, whether the target object has defects, and defect information of the target object. Compared to the size of the target object, users are often more concerned with where the target object is and its morphological integrity, thus enabling them to evaluate the target object. For instance, the location information of the target object in the detected image can help users identify the target object from the detected image. Defect information of the target object can also help users evaluate the target object.

[0066] Generating and displaying detected images containing target information about at least one of these detection results can better assist users in evaluating target objects.

[0067] For example, in a detected image, the image region where the target object is located can be rendered to identify the target object.

[0068] For example, in the detected image, the size of the target object can be identified in textual or graphic form.

[0069] For example, the aforementioned location information is marked in the detected image in the form of a bounding box, and / or the defect information of the target object is marked in the detected image in the form of text or images. Marking with bounding boxes makes it easier for users to observe the target object in the detected image compared to directly rendering the image area where the target object is located. Marking the defect information of the target object in the detected image in the form of text or images allows users to intuitively understand the defects of the target object, facilitating the evaluation of the target object.

[0070] Figure 4 A schematic diagram of a detected image is shown according to an embodiment of the present invention.

[0071] like Figure 4 As shown, the location information of the target object in each detected image can be marked with a detection box, such as the location of the electricity meter, the safety helmet, and the electricity meter window. Defects of the target object in each detected image can be marked with text, such as a tilted electricity meter, a missing window, or a missing seal.

[0072] In the above technical solution, a detected image containing target information about the detection results is generated and displayed based on the image to be detected and the detection results. This allows users to intuitively understand relevant information about the target object from the detected image, facilitating subsequent analysis of the target object.

[0073] For example, the mobile terminal back clip is equipped with a radio frequency identification module. Figure 5 A schematic flowchart illustrating the determination of a target object among at least one object to be inspected, according to an embodiment of the present invention, is shown. Figure 5 As shown, the detection method may further include steps S510 to S520.

[0074] In step S510, the mobile terminal back clip receives at least one radio frequency identification code (such as an RFID tag) of the object to be inspected, wherein the radio frequency identification module is used to identify the radio frequency identification code of the object to be inspected based on radio frequency identification technology.

[0075] An RFID tag can be attached to the object to be inspected. The mobile terminal clip can use an RFID module to transmit an RFID signal to identify the RFID tag's RFID code on the object to be inspected. For example, there can be one or more objects to be inspected.

[0076] In step S520, the target object is determined from at least one object to be inspected based on the received radio frequency identification code.

[0077] The received RFID tag code may correspond to multiple objects to be inspected, and these multiple objects to be inspected may belong to different types. At present, it may only be some of the objects to be inspected that meet the current inspection requirements. In this case, it is necessary to extract the objects to be inspected whose types meet the current inspection requirements as the target objects.

[0078] For example, the type of the object to be inspected can be determined based on the RFID tag code, and the object of the same type as the target object can be selected as the target object. For example, the types of the objects to be inspected include electricity meters and household appliances. If the type of the target object is an electricity meter, then the objects of the electricity meter type can be extracted as the target object.

[0079] For example, for each object to be inspected, it can be determined whether to perform artificial intelligence processing on that object, and the object to be inspected for which artificial intelligence processing is determined is identified as a target object, wherein there are multiple target objects.

[0080] In one embodiment, the RFID tags of the objects to be detected, which will be the target objects, can be predetermined for subsequent AI processing. Then, by comparing the received RFID tags, the objects that can be used as target objects are selected from at least one group of objects to be detected. Based on the received RFID tags, the current location of each object to be detected can be determined. This allows the location of the target object to be determined. It is understood that objects to be detected that can receive RFID tags will be located near the current location, and images can be captured from them normally. Objects to be detected that have not received RFID tags are too far from the current location, and even if they were target objects, it is highly unlikely that images could be captured from them at the current location; therefore, they can be excluded from being target images.

[0081] For example, in one embodiment, the radio frequency identification code of the object to be inspected can be pushed to the user so that the user can determine whether to perform artificial intelligence processing on the object to be inspected.

[0082] In step S120 above, the process of acquiring the image to be detected of the target object according to the artificial intelligence processing service can specifically include step S121: acquiring multiple images to be detected of the target object sequentially according to the artificial intelligence processing service. Since the target object is not far from the current location, it is more convenient to acquire multiple images to be detected of the target object sequentially. For example, an RFID tag can be received in real time from the back clip of the mobile terminal to determine the location of the target object, thereby capturing an image of it to obtain the image to be detected.

[0083] In the above technical solution, the mobile terminal's back clip receives the RFID tag of at least one object to be inspected. The RFID module identifies the RFID tag of the object to be inspected based on RFID technology, and determines the target object among the at least one object to be inspected based on the received RFID tag. This allows for the selection of the closest object to be inspected as the current target object by combining the RFID tag of the object being inspected, thus avoiding the inability to obtain an image of the target object, which could affect subsequent processing operations.

[0084] Figure 6 A schematic flowchart illustrating the receiving of processing results from non-AI processing services according to an embodiment of the present invention is provided. Figure 6 As shown, the detection method may further include steps S610 to S620.

[0085] In step S610, non-AI processing services executed by the mobile terminal back clip for the target object are determined.

[0086] Non-AI processing tasks are those that do not utilize AI models. For example, non-AI processing tasks performed on a target object may include read / write operations and data transformation operations. These tasks only involve data processing using traditional algorithms and do not utilize AI models.

[0087] In step S620, the processing result of the non-AI processing service is received from the back clip of the mobile terminal, wherein the detection of the image to be detected is performed after the non-AI processing service is completed.

[0088] The processing result can be feedback information after the mobile terminal clip performs non-AI processing on the target object, such as whether the non-AI processing was successfully executed, the data read or written in the read / write operation, and the data transformed by the data transformation operation.

[0089] Optionally, the mobile terminal can receive the processing results of non-AI processing services from the mobile terminal back clip via a data interface that communicates with the mobile terminal back clip, using any communication method.

[0090] For example, the mobile terminal back clip is equipped with an infrared recognition module. Non-AI processing services include reading and writing to a target object using the infrared recognition module of the mobile terminal back clip. Processing results can be received from the mobile terminal back clip via a preset communication method. These processing results include target information of the target object, which is read and written by the mobile terminal back clip through the execution of read and write operations.

[0091] The infrared recognition module can emit infrared recognition signals, and the mobile terminal clip can use these signals to read and write data on the target object to perform read / write operations. For example, the preset communication method could be one of Bluetooth communication, shared network, or serial communication. Understandably, using infrared recognition signals for communication reduces the hardware requirements of both the mobile terminal clip and the target object, lowers the power consumption requirements of the target object, and allows for point-to-point read / write operations on the target object. Receiving processing results from the mobile terminal clip based on the preset communication method avoids the risk of failing to receive processing results due to using incorrect communication methods.

[0092] Performing detection on the image to be detected after completing non-AI processing avoids delays in executing crucial non-AI tasks, leading to more accurate detection results. For example, non-AI processing might include preprocessing the image to improve its quality, verifying the correctness of the target object, and adjusting the parameters of the detection model used for detection. Performing detection on the image to be detected before completing non-AI processing could result in erroneous results. Therefore, this approach improves the effectiveness of image detection.

[0093] In the above technical solution, the non-AI processing tasks performed by the mobile terminal back clip on the target object are determined, and the processing results of the non-AI processing tasks are then received from the mobile terminal back clip. The detection of the image to be detected is performed after the completion of the non-AI processing tasks. This offloads the non-AI processing tasks to the mobile terminal back clip, reducing the operational burden and hardware requirements of the mobile terminal, while still ensuring that the mobile terminal accurately obtains both the processing results of the non-AI processing tasks and the detection results of the AI ​​processing tasks.

[0094] According to another aspect of the present invention, a mobile terminal is also provided, the mobile terminal being provided with a data interface for communicating with a mobile terminal back clip, the mobile terminal being used to perform the detection method described above.

[0095] According to another aspect of the present invention, an electronic device is also provided. Figure 7 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown. Figure 7As shown, the electronic device includes a processor and a memory, wherein the memory stores computer program instructions, which are executed by the processor to perform the detection method described above.

[0096] Furthermore, according to another aspect of the present invention, a storage medium is also provided, on which program instructions are stored. When the program instructions are executed by a computer or processor, the computer or processor performs the corresponding steps of the detection method described above in the embodiments of the present invention, and is used to implement the mobile terminal described above in the embodiments of the present invention. The storage medium may, for example, include a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0097] According to another aspect of the present invention, a computer program product is also provided, including computer program instructions, which, when executed, are used to perform the detection method described above.

[0098] Those skilled in the art can understand the specific implementation and beneficial effects of the above-described mobile terminal, electronic device, storage medium, and computer program products by reading the detailed description of the detection method above, and for the sake of brevity, they will not be described in detail here.

[0099] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of the invention. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of the invention. All such changes and modifications are intended to be included within the scope of the invention as claimed in the appended claims.

[0100] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0101] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0102] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0103] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of the invention. However, this approach should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with fewer features than all of those in a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0104] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus so disclosed can be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0105] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0106] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions for a mobile terminal according to embodiments of the present invention. The present invention can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing some or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium, or can take the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0107] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0108] The above description is merely a specific embodiment of the present invention or an explanation of that embodiment. The scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method of detection, characterized in that, The method is applied to a mobile terminal, the mobile terminal being provided with a data interface for communicating with a mobile terminal back clip, the method comprising: Identify the AI ​​processing tasks that need to be performed on the target object; Based on the aforementioned artificial intelligence processing, the image to be detected of the target object is acquired; Information about the artificial intelligence processing service and the image to be detected are transmitted to the mobile terminal back clip, so that the mobile terminal back clip can use the artificial intelligence model corresponding to the artificial intelligence processing service to detect the image to be detected; The detection results of the image to be detected are received and saved from the back clip of the mobile terminal.

2. The method of claim 1, wherein, According to the aforementioned artificial intelligence processing service, the image to be detected of the target object is acquired, including: When the image acquisition function of the mobile terminal is enabled, the image acquisition operation is performed at a preset frequency according to the artificial intelligence processing service to obtain the image to be detected.

3. The method of claim 1, wherein, The method further includes: Based on the image to be detected and the detection result, a detected image containing target information about the detection result is generated and displayed.

4. The method of claim 3, wherein, The detection result includes at least one of the following: the location information of the target object in the detected image, whether the target object has defects, and the defect information of the target object.

5. The method of claim 4, wherein, The location information is marked in the detected image in the form of a detection box, and / or the defect information of the target object is marked in the detected image in the form of text or pattern.

6. The method according to claim 1, characterized in that, The mobile terminal back clip is equipped with a radio frequency identification module, and the method further includes: The mobile terminal back clip receives at least one radio frequency identification (RFID) code of an object to be inspected, wherein the RFID module is used to identify the RFID code of the object to be inspected based on RFID technology. The target object is determined from the at least one object to be inspected based on the received radio frequency identification code.

7. The method according to claim 6, characterized in that, The step of determining the target object among the at least one object to be inspected based on the received radio frequency identification code includes: For each object to be inspected, it is determined whether the artificial intelligence processing service should be performed on that object, and the object to be inspected that is determined to require the artificial intelligence processing service is identified as the target object, wherein there are multiple target objects; The step of acquiring the image to be detected of the target object according to the artificial intelligence processing service includes: Based on the aforementioned artificial intelligence processing, the images of the multiple target objects to be detected are acquired sequentially.

8. The method according to claim 1, characterized in that, The method further includes: Determine the non-AI processing service performed by the mobile terminal clip on the target object; The processing result of the non-AI processing service is received from the back clip of the mobile terminal; The detection of the image to be detected is performed after the non-AI processing is completed.

9. The method according to claim 8, characterized in that, The mobile terminal back clip is equipped with an infrared recognition module. The non-AI processing services include reading and writing operations targeting the target object using the infrared recognition module of the mobile terminal back clip. The receiving of the processing result of the non-AI processing service from the back clip of the mobile terminal includes: The processing result is received from the mobile terminal back clip using a preset communication method. The processing result includes target information of the target object, wherein the target information is read and written by the mobile terminal back clip by executing the read and write service.

10. A mobile terminal, characterized in that, The mobile terminal is provided with a data interface for communicating with the mobile terminal back clip, and the mobile terminal is used to perform the detection method as described in any one of claims 1 to 9.

11. An electronic device comprising a processor and a memory, characterized in that, The memory stores computer program instructions, which, when executed by the processor, are used to perform the detection method as described in any one of claims 1 to 9.

12. A storage medium on which program instructions are stored, characterized in that, The program instructions are used to execute the detection method as described in any one of claims 1 to 9 when the program is run.

13. A computer program product comprising computer program instructions, characterized in that, The computer program instructions, when executed, are used to perform the detection method as described in any one of claims 1 to 9.

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