Method and device for optimizing express sign-in image

Abstract

The invention provides an optimization method and device for an express delivery signing image, and the method comprises the steps: receiving a signing image sent by a client, and carrying out the processing of the signing image, and obtaining a to-be-processed image; identifying a first defect area of the to-be-processed image; adjusting the first defect area image, and determining a loss value based on the first defect area and the normal area; the loss value represents a difference index between the first defect area and the real defect area; and extracting the features of the adjusted image, identifying the adjusted second defect area based on the features, and carrying out iterative optimization on the second defect area until the loss value is converged to obtain an optimized image. According to the method and the device, the signed image can be efficiently and accurately detected and optimized, a company can be helped to ensure that each photo meets the requirement, and the normalization and the efficiency of business operation can also be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of image processing, and in particular to a method and device for optimizing express delivery receipt images. Background Art

[0002] In today's rapidly developing express delivery industry, the traditional way of signing for parcels has gradually exposed many drawbacks and cannot meet the growing customer needs and the high efficiency requirements of industry development. Specifically, there are various ways to sign for express parcels, including door-to-door delivery, placement in express lockers or post stations, and delivery to designated locations. However, with the surge in the number of express parcels, in order to pursue delivery efficiency, couriers often choose to place parcels at a certain location and notify customers to pick them up. This leads to frequent cases of customers not being able to find express parcels, and the problem of couriers falsely signing for parcels is becoming increasingly serious, and the customer complaint rate has risen accordingly.

[0003] Therefore, some express delivery companies have tried to promote the photo signing method, but due to the lack of standardization and vividness in the photo-taking process, the image quality varies, and there is a lack of effective image quality detection mechanism, which makes it impossible to prevent the generation of errors or false images in advance. This not only fails to effectively improve the customer experience, but may also bring additional troubles and troubles to customers. Summary of the invention

[0004] The present disclosure provides a method and device for optimizing express delivery receipt images to at least solve the above technical problems existing in the prior art.

[0005] According to a first aspect of the present disclosure, a method for optimizing a courier receipt image is provided, the method comprising:

[0006] Receiving a receipt image sent by a client, and processing the receipt image to obtain an image to be processed;

[0007] Identifying a first defective area of ​​the image to be processed;

[0008] The first defect area image is adjusted, and a loss value is determined based on the first defect area and the normal area; the loss value represents a difference index between the first defect area and the real defect area;

[0009] Features of the adjusted image are extracted, a second defect region after adjustment is identified based on the features, and the second defect region is iteratively optimized until the loss value converges to obtain an optimized image.

[0010] In one possible implementation, the processing of the receipt image includes:

[0011] Affine transformation is performed on the received image to perform image enhancement to obtain an enhanced image; and the enhanced image is used as the image to be processed.

[0012] In one possible implementation manner, the identifying the first defective area of ​​the image to be processed includes:

[0013] The image to be processed is divided into images with multiple anchor point windows, and the image of each anchor point window is subjected to binary processing to identify the first defect area of ​​the signed image.

[0014] In one possible implementation, it further includes:

[0015] It is detected whether the image obtained after iterative optimization has an impurity region. If it exists, a non-maximum suppression method is used to remove the impurity region.

[0016] In one possible implementation manner, the adjusting the first defect area image includes:

[0017] The position and size of the defect area are adjusted.

[0018] In one possible implementation manner, the iterative optimization of the second defect region after the adjustment based on the feature identification includes:

[0019] identifying a first feature of a defective area and a second feature of a normal area in the adjusted image;

[0020] determining a correlation feature between the first feature and the second feature;

[0021] The first feature of the defect area is fused using the associated feature to optimize the defect area.

[0022] In one possible implementation, the receipt image is generated by taking a photo by the client.

[0023] According to a second aspect of the present disclosure, a device for optimizing a courier receipt image is provided, the device comprising:

[0024] A receiving module, used for receiving a receipt image sent by a client, and processing the receipt image to obtain an image to be processed;

[0025] An identification module, used for identifying a first defect area of ​​the image to be processed;

[0026] An adjustment module, configured to adjust the first defect area image and determine a loss value based on the first defect area and the normal area; the loss value represents a difference index between the first defect area and the real defect area;

[0027] The optimization module is used to extract features of the adjusted image, identify the adjusted second defect area based on the features, iteratively optimize the second defect area until the loss value converges, and obtain an optimized image.

[0028] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0029] at least one processor; and

[0030] a memory communicatively connected to the at least one processor; wherein,

[0031] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present disclosure.

[0032] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method described in the present disclosure.

[0033] The optimization method and device for express delivery receipt images disclosed in the present invention can efficiently and accurately detect and optimize the receipt images, which can not only help the company ensure that every photo meets the requirements, but also improve the standardization and efficiency of business operations.

[0034] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, in which:

[0036] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.

[0037] Figure 1 A schematic diagram of the implementation process of the express package signing method according to the embodiment of the present disclosure is shown;

[0038] Figure 2 A schematic diagram of the structure of the express package signing device according to an embodiment of the present disclosure is shown;

[0039] Figure 3 A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0040] In order to make the purpose, features, and advantages of the present disclosure more obvious and easy to understand, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.

[0041] Although some express delivery companies have launched the photo signing function, the operation mode is relatively simple with only images, and most existing products have problems such as unstable image quality, lack of unified standards and specifications, and inability to effectively prevent errors or false images. After completing the delivery, couriers usually need to take photos of the signing location as proof of delivery. However, the quality of these photos varies, and sometimes they may not meet the company's compliance requirements due to issues such as angle, light, and clarity. There are even some malicious pornographic images, which results in the limited effect of the photo signing function in actual applications. It is difficult to meet consumers' needs for efficient and accurate signing, affecting customer experience and courier work efficiency.

[0042] The following describes an optimization method and device for express delivery receipt images provided by an embodiment of the present application in conjunction with the accompanying drawings.

[0043] like Figure 1 As shown, the present application provides a method for optimizing express delivery receipt images, the method comprising:

[0044] S101, receiving a receipt image sent by a client, and processing the receipt image to obtain an image to be processed;

[0045] In some embodiments, the receipt image is generated by taking a photo by the client.

[0046] It is understandable that the optimization method for express delivery receipt images provided in the present application is implemented by a server. After receiving the receipt image sent by the client, the server first processes the receipt image to obtain an image to be processed. It is understandable that the client in the present application can be a handheld terminal held by the courier, such as a mobile phone, a tablet computer or other electronic device, or a handheld terminal specially used by the courier for receipt. After the courier takes a photo of the express package for receipt, the receipt image is obtained, and the server receives the receipt image sent by the handheld terminal.

[0047] It is understandable that the server in the present application can also perform quality inspection on the receipt image through a preset detection model to determine whether the receipt image meets the preset standard. If not, the receipt image is preprocessed to obtain the image to be processed. It should be noted that the detection model in the present application can adopt an existing model, or the detection model can detect whether the receipt image contains defective parts. If so, the receipt image is preprocessed to obtain the image to be processed.

[0048] The defects in the signed image include whether the express delivery note in the signed image is clear, whether the image clarity meets the standards, whether it involves pornography, etc.

[0049] S102, identifying a first defect area of ​​the image to be processed;

[0050] After preprocessing the received image, the image to be processed is obtained, and then the first defect area in the image to be processed can be identified through the recognition model. It should be noted that the recognition model adopts the existing model, and this application does not limit it here.

[0051] S103, adjusting the first defect area image, and determining a loss value based on the first defect area and the normal area; the loss value represents a difference index between the first defect area and the real defect area;

[0052] After determining the first defective area, the first defective area is adjusted, for example, the position and size of the defective area are adjusted, and then the defective area and the normal area are determined.

[0053] S104, extracting features of the adjusted image, identifying the adjusted second defect region based on the features, iteratively optimizing the second defect region until the loss value converges, and obtaining an optimized image.

[0054] Specifically, in the present application, after the first defect area is adjusted, the loss value between the first defect area and the normal area is reduced, and then the features of the adjusted image are extracted, and the features of the second defect area are adjusted using the features of the normal area, so that the features of the second defect area tend to the normal area, thereby reducing the loss value.

[0055] The optimization method for express delivery receipt images provided in this application automatically detects and evaluates the photos taken for receipt. The recognition model identifies key information in the photo, such as the shooting location, whether there is a receipt photo, clarity, whether it involves pornography, etc., to ensure that the photo quality meets the company's compliance requirements. It effectively avoids disputes and misunderstandings caused by photo quality issues, improves the standardization of business operations, and provides a strong guarantee for the company's compliance operations.

[0056] In some embodiments, the processing of the receipt image includes:

[0057] Affine transformation is performed on the received image to perform image enhancement to obtain an enhanced image; and the enhanced image is used as the image to be processed.

[0058] It is understandable that in order to enhance the quality and diversity of photos, the system uses Random affine transformation for image enhancement. This step can effectively improve the generalization ability of the model, enabling it to adapt to photos taken in different shooting environments and conditions.

[0059] In some embodiments, the step of identifying the first defective area of ​​the image to be processed includes:

[0060] The image to be processed is divided into images with multiple anchor point windows, and the image of each anchor point window is subjected to binary processing to identify the first defect area of ​​the signed image.

[0061] Specifically, in this application, the receipt image is divided into multiple anchor windows, and the system performs binary classification on each window. The purpose of this step is to identify areas that may contain non-compliance or defects. For example, whether there is a receipt photo in the photo, whether the receipt is complete, and whether the location is clear.

[0062] In some embodiments, the optimization method for express delivery receipt images provided by the present application further includes:

[0063] It is detected whether the image obtained after iterative optimization has an impurity region. If it exists, a non-maximum suppression method is used to remove the impurity region.

[0064] It should be noted that in order to avoid redundant or overlapping areas in the detection results, the system will use NMS (Non-Maximum Suppression) non-maximum suppression technology to remove these areas, ensuring that each non-compliant area is detected only once, thereby obtaining the most accurate and concise detection results.

[0065] In some embodiments, the adjusting the first defect area image includes:

[0066] The position and size of the defect area are adjusted.

[0067] Specifically, the server performs regression adjustment on these candidate non-compliant areas, calculating and adjusting the positions and sizes of these areas so that they more accurately reflect the actual defect locations. During this process, the system calculates the loss, which is a metric that quantifies the difference between defective areas and normal areas. By minimizing this loss, the system can more accurately locate non-compliant areas.

[0068] In some embodiments, the iterative optimization of the second defect region after the adjustment based on the feature identification includes:

[0069] identifying a first feature of a defective area and a second feature of a normal area in the adjusted image;

[0070] determining a correlation feature between the first feature and the second feature;

[0071] The first feature of the defect area is fused using the associated feature to optimize the defect area.

[0072] Specifically, in this application, the first feature of the defective area and the second feature of the normal area are first identified through the feature recognition model, the correlation feature between the first feature and the second feature is determined, and then the first feature of the defective area is fused through the correlation feature, so as to optimize the first feature and optimize the first defective area. The server performs iterative optimization to improve the accuracy of detection by continuously adjusting and refining candidate areas. Continuously converting and extracting higher-level features, these features help to more accurately identify and locate non-compliance in photos. After the non-compliant areas are determined, these areas will be further fine-tuned to ensure the accuracy of the detection results. At the same time, the target boxes of these areas will be regressed to determine the exact location of the non-compliant content.

[0073] The optimization method for express delivery receipt images provided in this application provides an efficient and accurate compliance detection tool for the courier's photo of receipt. It can not only help express delivery companies ensure that every photo meets compliance requirements, but also improve the standardization and efficiency of business operations.

[0074] like Figure 2 As shown, an embodiment of the present application provides an optimization device for express delivery receipt images, the device comprising:

[0075] The receiving module 201 is used to receive the receipt image sent by the client, and process the receipt image to obtain an image to be processed;

[0076] An identification module 202, configured to identify a first defective area of ​​the image to be processed;

[0077] An adjustment module 203 is used to adjust the first defect area image and determine a loss value based on the first defect area and the normal area; the loss value represents a difference index between the first defect area and the real defect area;

[0078] The optimization module 204 is used to extract features of the adjusted image, identify the adjusted second defect region based on the features, iteratively optimize the second defect region until the loss value converges, and obtain an optimized image.

[0079] The present application provides an optimization device for express delivery receipt images, wherein a receiving module 201 receives a receipt image sent by a client, and processes the receipt image to obtain an image to be processed; an identification module 202 identifies a first defect area of ​​the image to be processed; an adjustment module 203 adjusts the first defect area image, and determines a loss value based on the first defect area and a normal area; the loss value represents a difference index between the first defect area and a real defect area; an optimization module 204 extracts features of the adjusted image, identifies an adjusted second defect area based on the features, iteratively optimizes the second defect area until the loss value converges, and obtains an optimized image.

[0080] An embodiment of the present application provides an electronic device, including:

[0081] at least one processor; and

[0082] a memory communicatively connected to the at least one processor; wherein,

[0083] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present disclosure.

[0084] Figure 3 A schematic block diagram of an example electronic device 300 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0085] like Figure 3As shown, the device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0086] A number of components in the device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0087] The computing unit 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 301 performs the various methods and processes described above, such as an optimization method for express delivery receipt images. For example, in some embodiments, the optimization method for express delivery receipt images may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the optimization method for express delivery receipt images described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to execute the optimization method for express delivery receipt images in any other appropriate manner (eg, by means of firmware).

[0088] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0089] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0090] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0091] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0092] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0093] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0094] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0095] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0096] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for optimizing express delivery receipt images, characterized in that: The method comprises: Receiving a receipt image sent by a client, and processing the receipt image to obtain an image to be processed; Identifying a first defective area of ​​the image to be processed; The first defect area image is adjusted, and a loss value is determined based on the first defect area and the normal area; the loss value represents a difference index between the first defect area and the real defect area; Features of the adjusted image are extracted, a second defect region after adjustment is identified based on the features, and the second defect region is iteratively optimized until the loss value converges to obtain an optimized image.

2. The method according to claim 1, characterized in that The processing of the receipt image includes: Affine transformation is performed on the received image to perform image enhancement to obtain an enhanced image; and the enhanced image is used as the image to be processed.

3. The method according to claim 1, characterized in that The identifying a first defective area of ​​the image to be processed includes: The image to be processed is divided into images with multiple anchor point windows, and the image of each anchor point window is subjected to binary processing to identify the first defect area of ​​the signed image.

4. The method according to claim 1, characterized in that: Also includes: It is detected whether the image obtained after iterative optimization has an impurity region. If it exists, a non-maximum suppression method is used to remove the impurity region.

5. The method according to claim 1, characterized in that The adjusting the first defect area image includes: The position and size of the defect area are adjusted.

6. The method according to claim 1, characterized in that The iterative optimization of the second defect area after the adjustment based on the feature identification includes: identifying a first feature of a defective area and a second feature of a normal area in the adjusted image; determining a correlation feature between the first feature and the second feature; The first feature of the defect area is fused using the associated feature to optimize the defect area.

7. The method according to claim 1, characterized in that The receipt image is generated by taking a photo by the client.

8. An optimization device for express delivery receipt images, characterized in that: The device comprises: A receiving module, used for receiving a receipt image sent by a client, and processing the receipt image to obtain an image to be processed; An identification module, used for identifying a first defect area of ​​the image to be processed; An adjustment module, configured to adjust the first defect area image and determine a loss value based on the first defect area and the normal area; the loss value represents a difference index between the first defect area and the real defect area; The optimization module is used to extract features of the adjusted image, identify the adjusted second defect area based on the features, iteratively optimize the second defect area until the loss value converges, and obtain an optimized image.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: include: A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

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