Detection method, apparatus, device, and storage medium

Abstract

The present disclosure provides a detection method, device, equipment and storage medium, relating to the field of artificial intelligence. The method comprises: obtaining a first to-be-detected image, processing the to-be-detected image based on a first target detection algorithm to obtain a target detection region in the to-be-detected image, performing transformation processing on the target detection region to obtain a plurality of second to-be-detected images, processing the second to-be-detected images based on a second target detection algorithm to obtain a recognition result and a confidence level of the text in the second to-be-detected images, and fusing the plurality of recognition results and the confidence levels to obtain a detection result. Through processing of the to-be-detected image by a plurality of intelligent agents, the accuracy of recognition can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence, and in particular to a detection method, apparatus, device, and storage medium. Background Technology

[0002] With the development of artificial intelligence, its application in various fields is becoming increasingly widespread. In the industrial sector, industrial structural components are often identified by affixing labels. However, relying on quality inspectors to identify these labels typically leads to a waste of human resources. Furthermore, due to the varying locations of these labels on structural components, AI-based identification methods can result in inaccurate recognition. Summary of the Invention

[0003] This disclosure provides a detection method, apparatus, device, and storage medium that improves detection accuracy to at least a certain extent.

[0004] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0005] According to one aspect of this disclosure, a detection method is provided, comprising: Obtain the first image to be detected; The image to be detected is processed based on the first target detection algorithm to obtain the target detection region in the image to be detected; The target detection region is transformed to obtain multiple second images to be detected. The second target detection algorithm is used to process the second image to be detected, and the recognition results and confidence scores of the text in the second image to be detected are obtained. The detection result is obtained by fusing multiple recognition results and confidence scores.

[0006] In one embodiment of this disclosure, the method further includes: Multiple recognition results are transformed to obtain the transformed recognition result; By fusing multiple identification results and confidence levels, the detection result is obtained, including: The detection result is obtained by fusing the multiple transformed recognition results and confidence scores.

[0007] In one embodiment of this disclosure, the target detection region is transformed to obtain multiple second images to be detected, including: The target detection region is scaled at multiple scales to obtain multiple scaled target detection regions; Multiple scaled target detection regions are rotated at multiple scales to obtain multiple second images to be detected.

[0008] In one embodiment of this disclosure, the recognition results include text content, text box coordinates, scaling ratio, and rotation angle.

[0009] In one embodiment of this disclosure, multiple identification results and confidence levels are fused to obtain a detection result, including: The coordinates of the text box are corrected based on the scaling ratio and rotation angle to obtain the corrected text box. Map multiple corrected text boxes to the same coordinate system to ensure that the positions of the multiple text boxes are consistent. The confidence scores corresponding to the text content within the overlapping areas are weighted. The detection result is determined based on the weighted confidence level and the text content.

[0010] In one embodiment of this disclosure, the method further includes: If at least one of the multiple test results differs from the remaining test results, the test results are classified into multiple categories. Label the test results within categories that contain fewer test results.

[0011] In one embodiment of this disclosure, the method further includes: If at least one of the multiple test results differs from the remaining test results, the test results are classified into multiple categories. Repeat the detection of the first image to be detected corresponding to the detection results in the category with fewer detection results.

[0012] According to another aspect of this disclosure, a detection apparatus is provided, comprising: The acquisition module is used to acquire the first image to be detected; The first processing module is used to process the image to be detected based on the first target detection algorithm to obtain the target detection region in the image to be detected; The first transformation module is used to transform the target detection region to obtain multiple second images to be detected; The second processing module is used to process the second image to be detected based on the second target detection algorithm to obtain the recognition result and confidence level of the text in the second image to be detected. The first fusion module is used to fuse multiple recognition results and confidence scores to obtain the detection result.

[0013] In one embodiment of this disclosure, an acquisition module is used to acquire a first image to be detected; The first processing module is used to process the image to be detected based on the first target detection algorithm to obtain the target detection region in the image to be detected; The first transformation module is used to transform the target detection region to obtain multiple second images to be detected; The second processing module is used to process the second image to be detected based on the second target detection algorithm to obtain the recognition result and confidence level of the text in the second image to be detected. The first fusion module is used to fuse multiple recognition results and confidence scores to obtain the detection result.

[0014] In one embodiment of this disclosure, the apparatus further includes: The second transformation module is used to transform multiple recognition results to obtain the transformed recognition results; The first fusion module includes: The first fusion unit is used to fuse multiple transformed recognition results and confidence scores to obtain the detection result.

[0015] In one embodiment of this disclosure, the first transformation module includes: The scaling unit is used to scale the target detection region at multiple scales to obtain multiple scaled target detection regions. The rotation unit is used to rotate multiple scaled target detection regions at multiple scales to obtain multiple second images to be detected.

[0016] In one embodiment of this disclosure, the recognition results include text content, text box coordinates, scaling ratio, and rotation angle.

[0017] In one embodiment of this disclosure, the first fusion module includes: The correction unit is used to correct the coordinates of the text box based on the scaling ratio and rotation angle to obtain the corrected text box. The mapping unit is used to map multiple corrected text boxes to the same coordinate system so that the positions of the multiple text boxes are consistent. The weighting unit is used to weight the confidence scores of the text content within the overlapping region. The determination unit is used to determine the detection result based on the weighted confidence level and the text content.

[0018] In one embodiment of this disclosure, the apparatus further includes: The classification module is used to classify the detection results into multiple categories when at least one detection result differs from the remaining detection results. The tagging module is used to tag detection results within categories that contain fewer detection results.

[0019] In one embodiment of this disclosure, the apparatus further includes: The classification module is used to classify the detection results into multiple categories when at least one detection result differs from the remaining detection results. The repetition module is used to repeatedly detect the first image to be detected, which contains fewer detection results in the category.

[0020] According to another aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the detection method described above by executing the executable instructions.

[0021] According to another aspect of this disclosure, a computer-readable storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the detection method described above.

[0022] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the detection method described above.

[0023] The detection method, apparatus, device, and storage medium provided in the embodiments of this disclosure acquire a first image to be detected, process the image to be detected based on a first target detection algorithm to obtain a target detection region in the image to be detected, transform the target detection region to obtain multiple second images to be detected, process the second images to be detected based on a second target detection algorithm to obtain the recognition result and confidence level of the text in the second images to be detected, and fuse the multiple recognition results and confidence levels to obtain the detection result. By using multiple intelligent agents to process the images to be detected, the accuracy of recognition can be improved.

[0024] 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

[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0026] Figure 1 This diagram illustrates an architecture of a detection system according to an embodiment of the present disclosure. Figure 2 This diagram illustrates a detection method flowchart according to an embodiment of the present disclosure; Figure 3 This diagram illustrates another detection method flowchart in an embodiment of the present disclosure; Figure 4 This diagram illustrates a flowchart of yet another detection method according to an embodiment of the present disclosure; Figure 5 This diagram illustrates a flowchart of yet another detection method according to an embodiment of the present disclosure; Figure 6 This diagram illustrates a flowchart of yet another detection method according to an embodiment of the present disclosure; Figure 7 This diagram illustrates a flowchart of yet another detection method according to an embodiment of the present disclosure; Figure 8 A structural diagram of a detection device according to an embodiment of this disclosure is shown; Figure 9 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0027] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0028] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0029] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0030] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0031] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0032] It should be noted that, where there is no conflict, the embodiments of this disclosure and the technical features in the embodiments can be combined with each other.

[0033] In the production and warehousing management of industrial steel, label text serves as crucial information for batch numbers, specifications, production dates, and quality control, and is widely used for logistics traceability and quality inspection. Quality inspectors need to determine whether a bundle of steel belongs to the same batch based on the label information. However, in reality, due to the disorganized stacking of steel, different labels often exist within the same bundle. In such cases, workers must visually compare the label information one by one to determine if the bundle has been mixed with products from different batches. If inconsistent label information is found, the corresponding steel needs to be removed to ensure consistency within the same batch.

[0034] The specific implementation of the embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0035] Figure 1 A schematic diagram of a detection system structure is shown in an embodiment of the present disclosure. This system can apply the detection methods or detection processing devices in various embodiments of the present disclosure.

[0036] like Figure 1 As shown, the detection system 10 may include a data acquisition module 101 and a detection module 102. The data acquisition module 101 and the detection module 102 may be located on two different devices. The data acquisition module 101 may be a module on an electronic device with data acquisition capabilities, such as a recording device with sound collection capabilities, a photographic device with image collection capabilities, or a computer with text information collection capabilities. The detection module 102 may be a module on an electronic device with processing capabilities, such as a computer. The data acquisition module 101 and the detection module 102 may also be located on the same device. For example, the data acquisition module 101 and the detection module 102 may be an input module and a processing module on a computer or mobile phone.

[0037] The acquisition module 101 and the detection module 102 are connected by a network, which can be a wired network or a wireless network.

[0038] Optionally, the aforementioned wireless or wired networks use standard communication technologies and / or protocols. The network is typically the Internet, but can also be any network, including but not limited to Local Area Networks (LANs), Metropolitan Area Networks (MANs), Wide Area Networks (WANs), mobile, wired or wireless networks, private networks, or any combination of virtual private networks. In some embodiments, technologies and / or formats including Hyper Text Markup Language (HTML), Extensible Markup Language (XML), etc., are used to represent data exchanged over the network. Furthermore, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Networks (VPNs), and Internet Protocol Security (IPsec) can be used to encrypt all or some links. In other embodiments, custom and / or dedicated data communication technologies can be used to replace or supplement the aforementioned data communication technologies.

[0039] The following describes the case where the acquisition module 101 and the detection module 102 are located on two different devices.

[0040] The acquisition module 101 can be located on a terminal device, which can be various electronic devices, including but not limited to smartphones, tablets, laptops, desktop computers, wearable devices, augmented reality devices, virtual reality devices, etc.

[0041] Optionally, the client for the application installed on different terminal devices can be the same, or the client for the same type of application based on different operating systems. Depending on the terminal platform, the specific form of the application client can also differ; for example, the application client can be a mobile client, a PC client, etc.

[0042] The detection module 102 can be located on a server, which can be a server that provides various services, such as a backend management server that supports the operation of the device by the user using the terminal device. The backend management server can analyze and process the received request data and feed the processing results back to the terminal device.

[0043] Optionally, the server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0044] Those skilled in the art will know that Figure 1 The number of clients and proxies shown is merely illustrative; any number of clients and proxies can be used as needed. This disclosure does not limit this.

[0045] To address the aforementioned problems, this disclosure provides a detection method, apparatus, device, and storage medium.

[0046] Figure 2 A flowchart of a detection method according to an embodiment of this disclosure is shown.

[0047] like Figure 2 As shown, the method may include: S210, acquire the first image to be detected.

[0048] In some embodiments, the first image to be detected may include an image containing a target to be detected. The target to be detected may contain text information. For example, the target to be detected may include a label. The first image to be detected may include an image containing a label.

[0049] In some embodiments, a first image to be detected may be acquired by a camera device, which may include a smart device containing a camera.

[0050] S220, the first image to be detected is processed based on the first target detection algorithm to obtain the target detection region in the first image to be detected.

[0051] In some embodiments, the first object detection algorithm may include an algorithm for identifying the location and category of a target region in an image. The first object detection algorithm may be a real-time object detection algorithm. A real-time object detection algorithm may be the You Only Look Once (YOLO) algorithm.

[0052] For example, the YOLO algorithm can divide an image into multiple grids. Each grid is responsible for predicting the object whose center point falls within that grid. Each grid predicts multiple bounding boxes, along with the confidence score for each bounding box and the probabilities of multiple classes. The YOLO algorithm ultimately outputs a tensor, which is then used with Non-Maximum Suppression (NMS) to remove duplicate bounding boxes.

[0053] In some embodiments, processing the first image to be detected based on the first target detection algorithm may include inputting the first image to be detected into the first target detection algorithm.

[0054] In some embodiments, the target detection area can be the area corresponding to the target to be detected.

[0055] S230, transform the target detection area to obtain multiple second images to be detected.

[0056] In some embodiments, the transformation process may include a region that physically alters the target detection region. For example, the transformation process may include at least one of translation, flipping, shearing, perspective transformation, and cropping.

[0057] In some embodiments, the second image to be detected may include the image after the above transformation process.

[0058] In some embodiments, the target detection area can be passed through

[0059] S240, The second image to be detected is processed based on the second target detection algorithm to obtain the recognition result and confidence level of the text in the second image to be detected; S250 integrates multiple recognition results and confidence scores to obtain the detection result.

[0060] In this embodiment, a first image to be detected is acquired, and the image is processed based on a first object detection algorithm to obtain an object detection region in the image. The object detection region is then transformed to obtain multiple second images to be detected. These second images are then processed based on a second object detection algorithm to obtain the recognition results and confidence scores of the text in the second images. Finally, the multiple recognition results and confidence scores are fused to obtain the detection result. Processing the image to be detected using multiple agents improves the accuracy of the recognition.

[0061] Figure 3 A flowchart of another detection method in an embodiment of this disclosure is shown.

[0062] like Figure 3 As shown, the method may include: S310, acquire the first image to be detected; S320, The first image to be detected is processed based on the first target detection algorithm to obtain the target detection region in the first image to be detected; S330, transform the target detection region to obtain multiple second images to be detected; S340, The second image to be detected is processed based on the second target detection algorithm to obtain the recognition result and confidence level of the text in the second image to be detected; S350 transforms multiple recognition results to obtain the transformed recognition result.

[0063] In some embodiments, transforming the recognition result may include performing a physical transformation on the image, and the transformation process may include at least one of translation, flipping, cropping, perspective transformation, and clipping.

[0064] S360 fuses multiple transformed recognition results and confidence scores to obtain the detection result.

[0065] In some embodiments, the method for fusing multiple transformed recognition results and confidence scores is the same as the method for fusing multiple recognition results and confidence scores in the above embodiments, and will not be repeated here.

[0066] In this embodiment, a first image to be detected is acquired. The image is then processed using a first object detection algorithm to obtain a target detection region. This target detection region is then transformed to obtain multiple second images to be detected. These second images are then processed using a second object detection algorithm to obtain the recognition results and confidence scores of the text within them. The multiple recognition results are then transformed to obtain a transformed recognition result. Finally, the transformed recognition result and the confidence score are fused to obtain the detection result. By using multiple agents to process the image to be detected, the accuracy of the recognition can be improved.

[0067] Figure 4 A flowchart of another detection method according to an embodiment of this disclosure is shown.

[0068] S410, acquire the first image to be detected; S420, The first image to be detected is processed based on the first target detection algorithm to obtain the target detection region in the first image to be detected; S430 performs scaling on the target detection area at multiple scales to obtain multiple scaled target detection areas.

[0069] In some embodiments, each target detection region can be scaled to obtain a corresponding scaled target detection region. Multiple scaled target detection regions can be obtained by scaling the target detection regions at multiple scales.

[0070] For example, the scaling factor can be 0.75, 0.5, 1.5, 2, etc.

[0071] S440 rotates multiple scaled target detection regions at multiple scales to obtain multiple second images to be detected.

[0072] In some embodiments, performing rotation processing at each scale can yield the corresponding scaled target detection region after rotation.

[0073] For example, the rotation angle can be 0, 90, 180, or 270 degrees.

[0074] S450, The second image to be detected is processed based on the second target detection algorithm to obtain the recognition result and confidence level of the text in the second image to be detected; S460 integrates multiple recognition results and confidence scores to obtain the detection result.

[0075] In some embodiments, the recognition results include text content, text box coordinates, scaling ratio, and rotation angle.

[0076] It should be noted that the text content may include text and / or image information. The text box can be a bounding box capable of containing the aforementioned text content. The scaling ratio can be the scaling ratio described in the above embodiments, and the rotation angle can be the rotation angle described above.

[0077] In this embodiment, a first image to be detected is acquired. The image is then processed using a first object detection algorithm to obtain object detection regions. These regions are scaled at multiple scales to obtain multiple scaled object detection regions. The scaled object detection regions are then rotated at multiple scales to obtain multiple second images to be detected. These second images are processed using a second object detection algorithm to obtain the recognition results and confidence scores of the text in the second images. The multiple recognition results are then transformed to obtain transformed recognition results. Finally, the transformed recognition results and confidence scores are fused to obtain the detection result. By using multiple intelligent agents to process the images to be detected, the accuracy of the recognition can be improved.

[0078] Figure 5 A flowchart of another detection method according to an embodiment of this disclosure is shown.

[0079] like Figure 5 As shown, the method may include: S510, acquire the first image to be detected; S520, the first image to be detected is processed based on the first target detection algorithm to obtain the target detection region in the first image to be detected; S530, transform the target detection region to obtain multiple second images to be detected; S540, The second image to be detected is processed based on the second target detection algorithm to obtain the recognition result and confidence level of the text in the second image to be detected; S550 performs coordinate correction on the text box based on the scaling ratio and rotation angle to obtain the corrected text box.

[0080] S560 maps multiple corrected text boxes to the same coordinate system to ensure that the positions of the multiple text boxes are consistent. S570, weighted the confidence scores corresponding to the text content within the overlapping area.

[0081] In some embodiments, this disclosure does not limit the weighted data.

[0082] S580 determines the detection result based on the weighted confidence level and the text content.

[0083] In this embodiment, a first image to be detected is acquired. The image is then processed using a first object detection algorithm to obtain object detection regions. These regions are scaled at multiple scales to obtain multiple scaled object detection regions. These scaled regions are then rotated at multiple scales to obtain multiple second images to be detected. A second object detection algorithm is then used to process these second images to obtain text recognition results and confidence scores. These recognition results are then transformed to obtain transformed recognition results. The coordinates of the text boxes are corrected based on the scaling ratio and rotation angle to obtain corrected text boxes. These corrected text boxes are mapped to the same coordinate system to ensure consistent positions. The confidence scores corresponding to the text content within overlapping areas are weighted. The detection result is determined based on the weighted confidence scores and the text content. Using multiple agents to process the image to be detected improves the accuracy of the recognition.

[0084] Figure 6 A flowchart of another detection method in an embodiment of this disclosure is shown.

[0085] like Figure 6 As shown, the method may include: S610, acquire the first image to be detected; S620, the first image to be detected is processed based on the first target detection algorithm to obtain the target detection region in the first image to be detected; S630, transform the target detection region to obtain multiple second images to be detected; S640, The second image to be detected is processed based on the second target detection algorithm to obtain the recognition result and confidence level of the text in the second image to be detected; The S650 integrates multiple recognition results and confidence scores to obtain the detection result.

[0086] S660: When at least one of the multiple detection results differs from the remaining detection results, the detection results are classified into multiple categories.

[0087] In some embodiments, multiple categories of detection results may exist.

[0088] S670 will label the test results within categories that contain fewer test results.

[0089] In some embodiments, the detection results can be marked in various ways. This disclosure does not limit the marking method, which may include highlighting, color coding, etc. In this embodiment, a first image to be detected is acquired, and the image is processed based on a first object detection algorithm to obtain an object detection region in the image. The object detection region is then transformed to obtain multiple second images to be detected. These second images are then processed based on a second object detection algorithm to obtain the recognition results and confidence scores of the text in the second images. The multiple recognition results and confidence scores are then fused to obtain the detection result. By using multiple agents to process the image to be detected, and in cases where at least one detection result differs from the remaining detection results, the detection results are classified into multiple categories. Detection results within categories containing fewer detection results are labeled, which improves the accuracy of recognition.

[0090] Figure 7 A flowchart of another detection method in an embodiment of this disclosure is shown.

[0091] like Figure 7 As shown, the method may include: S710, acquire the first image to be detected; S720, Based on the first target detection algorithm, process the first image to be detected to obtain the target detection region in the first image to be detected; S730 performs transformation processing on the target detection area to obtain multiple second images to be detected; S740, based on the second target detection algorithm, the second image to be detected is processed to obtain the recognition result and confidence level of the text in the second image to be detected; The S750 fuses multiple recognition results and confidence scores to obtain the detection result.

[0092] S760: When at least one of the multiple detection results differs from the remaining detection results, the detection results are classified into multiple categories.

[0093] In some embodiments, multiple categories of detection results may exist.

[0094] S770 repeats the detection of the first image to be detected corresponding to the detection results in the category with fewer detection results.

[0095] In some embodiments, the method for repeated detection can be the same as the method for the first detection, which will not be described in detail here.

[0096] In this embodiment, a first image to be detected is acquired. The image is then processed using a first object detection algorithm to obtain a target detection region. This target detection region is then transformed to obtain multiple second images to be detected. These second images are then processed using a second object detection algorithm to obtain the recognition results and confidence scores of the text within them. The multiple recognition results and confidence scores are then fused to obtain the detection result. Multiple agents process the images to be detected. If at least one detection result differs from the remaining detection results, the detection results are classified into multiple categories. The first image to be detected corresponding to the detection results in the category with fewer detection results is then repeatedly detected.

[0097] Based on the same inventive concept, this disclosure also provides a detection device, as shown in the following embodiment. Since the principle by which this device embodiment solves the problem is similar to that of the above-described method embodiment, the implementation of this device embodiment can refer to the implementation of the above-described method embodiment, and repeated details will not be elaborated further.

[0098] Figure 8 A structural diagram of a detection device according to an embodiment of this disclosure is shown.

[0099] like Figure 8 As shown, the device 800 may include: an acquisition module 810, a first processing module 820, a first transformation module 830, a second processing module 840, and a first fusion module 850.

[0100] The acquisition module 810 is used to acquire the first image to be detected; The first processing module 820 is used to process the image to be detected based on the first target detection algorithm to obtain the target detection region in the image to be detected; The first transformation module 830 is used to transform the target detection region to obtain multiple second images to be detected; The second processing module 840 is used to process the second image to be detected based on the second target detection algorithm to obtain the recognition result and confidence level of the text in the second image to be detected. The first fusion module 850 is used to fuse multiple recognition results and confidence scores to obtain the detection result.

[0101] In one embodiment of this disclosure, an acquisition module is used to acquire a first image to be detected; The first processing module is used to process the image to be detected based on the first target detection algorithm to obtain the target detection region in the image to be detected; The first transformation module is used to transform the target detection region to obtain multiple second images to be detected; The second processing module is used to process the second image to be detected based on the second target detection algorithm to obtain the recognition result and confidence level of the text in the second image to be detected. The first fusion module is used to fuse multiple recognition results and confidence scores to obtain the detection result.

[0102] In one embodiment of this disclosure, the apparatus further includes: The second transformation module is used to transform multiple recognition results to obtain the transformed recognition results; The first fusion module includes: The first fusion unit is used to fuse multiple transformed recognition results and confidence scores to obtain the detection result.

[0103] In one embodiment of this disclosure, the first transformation module includes: The scaling unit is used to scale the target detection region at multiple scales to obtain multiple scaled target detection regions. The rotation unit is used to rotate multiple scaled target detection regions at multiple scales to obtain multiple second images to be detected.

[0104] In one embodiment of this disclosure, the recognition results include text content, text box coordinates, scaling ratio, and rotation angle.

[0105] In one embodiment of this disclosure, the first fusion module includes: The correction unit is used to correct the coordinates of the text box based on the scaling ratio and rotation angle to obtain the corrected text box. The mapping unit is used to map multiple corrected text boxes to the same coordinate system so that the positions of the multiple text boxes are consistent. The weighting unit is used to weight the confidence scores of the text content within the overlapping region. The determination unit is used to determine the detection result based on the weighted confidence level and the text content.

[0106] In one embodiment of this disclosure, the apparatus further includes: The classification module is used to classify the detection results into multiple categories when at least one detection result differs from the remaining detection results. The tagging module is used to tag detection results within categories that contain fewer detection results.

[0107] In one embodiment of this disclosure, the apparatus further includes: The classification module is used to classify the detection results into multiple categories when at least one detection result differs from the remaining detection results. The repetition module is used to repeatedly detect the first image to be detected, which contains fewer detection results in the category.

[0108] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0109] The following reference Figure 9 To describe an electronic device 900 according to such an embodiment of the present disclosure. Figure 9 The electronic device 900 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0110] like Figure 9 As shown, the electronic device 900 is manifested in the form of a general-purpose computing device. The components of the electronic device 900 may include, but are not limited to: at least one processing unit 910, at least one storage unit 920, and a bus 930 connecting different system components (including storage unit 920 and processing unit 910).

[0111] The storage unit stores program code, which can be executed by the processing unit 910, causing the processing unit 910 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. For example, the processing unit 910 can perform the following steps of the above method embodiments: Obtain the first image to be detected; The first target detection algorithm is used to process the first image to be detected to obtain the target detection region in the first image to be detected; The target detection region is transformed to obtain multiple second images to be detected. The second target detection algorithm is used to process the second image to be detected, and the recognition results and confidence scores of the text in the second image to be detected are obtained. The detection result is obtained by fusing multiple recognition results and confidence scores.

[0112] Storage unit 920 may include readable media in the form of volatile storage units, such as random access memory (RAM) 9201 and / or cache memory 9202, and may further include read-only memory (ROM) 9203.

[0113] The storage unit 920 may also include a program / utility 9204 having a set (at least one) program module 9205, such program module 9205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0114] Bus 930 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0115] Electronic device 900 can also communicate with one or more external devices 940 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 900, and / or with any device that enables electronic device 900 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 950. Furthermore, electronic device 900 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 960. As shown, network adapter 960 communicates with other modules of electronic device 900 via bus 930. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0116] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0117] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. A program product capable of implementing the methods described above is stored thereon. In some possible implementations, various aspects of this disclosure may also be implemented as a program product including program code, which, when run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0118] More specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0119] In this disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, wherein readable program code is carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0120] Optionally, the program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0121] In practice, program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0122] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0123] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0124] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0125] 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 disclosure 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.

Claims

1. A detection method, characterized in that, include: Obtain the first image to be detected; The first target detection image is processed based on the first target detection algorithm to obtain the target detection region in the first target detection image; The target detection region is transformed to obtain multiple second images to be detected. The second target detection algorithm is used to process the second image to be detected, and the recognition result and confidence level of the text in the second image to be detected are obtained. The detection result is obtained by fusing multiple recognition results and confidence scores.

2. The method according to claim 1, characterized in that, The method further includes: The multiple recognition results are transformed to obtain the transformed recognition results; The process of fusing multiple identification results and confidence levels to obtain detection results includes: The detection result is obtained by fusing the multiple transformed recognition results and confidence scores.

3. The method according to claim 1, characterized in that, The transformation processing of the target detection region yields multiple second images to be detected, including: The target detection region is scaled at multiple scales to obtain multiple scaled target detection regions; Multiple scaled target detection regions are rotated at multiple scales to obtain multiple second images to be detected.

4. The method according to claim 3, characterized in that, The recognition results include text content, text box coordinates, scaling ratio, and rotation angle.

5. The method according to claim 4, characterized in that, The process of fusing multiple identification results and confidence levels to obtain detection results includes: Based on the scaling ratio and rotation angle, the coordinates of the text box are corrected to obtain the corrected text box. Map multiple corrected text boxes to the same coordinate system to ensure that the positions of the multiple text boxes are consistent. The confidence scores corresponding to the text content within the overlapping areas are weighted. The detection result is determined based on the weighted confidence level and the text content.

6. The method according to claim 1, characterized in that, The method further includes: When at least one of the multiple test results differs from the remaining test results, the test results are classified into multiple categories. Label the test results within categories that contain fewer test results.

7. The method according to claim 1, characterized in that, The method further includes: When at least one of the multiple test results differs from the remaining test results, the test results are classified into multiple categories. Repeat the detection of the first image to be detected corresponding to the detection results in the category with fewer detection results.

8. A detection device, characterized in that, Applied to the proxy side, including: The acquisition module is used to acquire the first image to be detected; The first processing module is used to process the image to be detected based on the first target detection algorithm to obtain the target detection region in the image to be detected; The first transformation module is used to transform the target detection region to obtain multiple second images to be detected; The second processing module is used to process the second image to be detected based on the second target detection algorithm to obtain the recognition result and confidence level of the text in the second image to be detected. The first fusion module is used to fuse multiple recognition results and confidence scores to obtain the detection result.

9. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the detection method according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the detection method according to any one of claims 1 to 7.

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