A logistics tracking system and method

Abstract

The application provides a logistics tracking system and method. In the logistics tracking system, a logistics tracking edge device first performs edge detection and filtering on a bar code collection image to obtain a bar code enhanced image; performs bar code reference separation on the bar code collection image to obtain a plurality of bar code reset reference subgraphs; further determines a reference confidence weight corresponding to the bar code reset reference subgraph according to a reference contrast and a reference direction factor corresponding to the bar code reset reference subgraph; performs reset fusion on reset rotation features respectively corresponding to each bar code reset reference subgraph to obtain a reference reset angle of the bar code; and finally resets and detects the bar code enhanced image according to the reference reset angle, and sends a detection result to the cloud. The application can accurately reset the logistics tracking bar code when a reference object attached to the logistics tracking bar code is distorted, and improve the recognition rate of the logistics tracking bar code.

Description

Technical Field

[0001] This application relates to the field of logistics tracking technology, and more specifically, to a logistics tracking system and method, such as terminal equipment, chips, computer storage media, etc. Background Technology

[0002] In logistics tracking systems, barcodes are crucial information carriers and a core link connecting physical goods with digital management systems. They solve the core problems of asynchronous goods and information in logistics, as well as the inefficiency and error-proneness of manual recording, greatly improving the efficiency and accuracy of logistics processing. However, with the increasing complexity of application scenarios, logistics tracking barcode detection technology in logistics tracking systems is facing unprecedented challenges.

[0003] Traditional barcode detection technology for logistics tracking typically involves identifying the barcode area and then rotating and repositioning it based on its rectangularity or tilt angle before recognition. This method fails to consider the distortion caused by the reference object to which the barcode is attached. When the object to which the barcode is attached is damaged or significantly distorted, the lack of background makes barcode detection difficult, leading to excessively long detection times or even failure to detect the barcode altogether. This severely limits the expanded application of logistics tracking barcodes. Summary of the Invention

[0004] This application provides a logistics tracking system and method to solve the technical problem of difficulty in resetting the logistics tracking barcode when the reference object to which the logistics tracking barcode is attached is distorted.

[0005] Firstly, this application provides a method for resetting and detecting logistics tracking barcodes, applied to a logistics tracking system. The logistics tracking system adopts a cloud-edge collaborative architecture, which includes logistics tracking edge devices and the cloud.

[0006] Specifically, the method includes:

[0007] The logistics tracking edge device initiates a logistics tracking barcode reset detection and acquires a barcode image.

[0008] Edge detection and filtering are performed on the acquired barcode image to obtain an enhanced barcode image;

[0009] The barcode acquisition image is subjected to barcode reference separation to obtain multiple barcode reset reference sub-images;

[0010] For any barcode reset reference sub-image, determine the reference contrast and reference orientation factor corresponding to the barcode reset reference sub-image, and then determine the reference confidence weight corresponding to the barcode reset reference sub-image based on the reference contrast and reference orientation factor corresponding to the barcode reset reference sub-image.

[0011] Determine the reset rotation features corresponding to each barcode reset reference sub-image, and perform reset fusion on the reset rotation features corresponding to each barcode reset reference sub-image according to the reference confidence weight of each barcode reset reference sub-image to obtain the reference reset angle of the barcode;

[0012] The barcode enhancement image is reset and detected according to the reference reset angle, and the detection result is sent to the cloud.

[0013] In conjunction with the first aspect, in certain implementations of the first aspect, performing edge detection and filtering on the acquired barcode image to obtain an enhanced barcode image specifically includes:

[0014] The captured barcode image is subjected to grayscale binarization to obtain a black and white image of the barcode;

[0015] The outline of the barcode black and white image is extracted to obtain the barcode outline features. Based on the barcode outline features, the acquired barcode image is segmented and filtered to obtain an enhanced barcode image.

[0016] In conjunction with the first aspect, in certain implementations of the first aspect, performing barcode reference separation on the acquired barcode image to obtain multiple barcode reset reference sub-images specifically includes:

[0017] The barcode image is subjected to reference background recognition to obtain multiple reference background edge points;

[0018] Multiple reference background edge groups are obtained by grouping the points based on their positional relationships.

[0019] For any reference background edge group, background segmentation is performed based on all reference background edge points within the reference background edge group to obtain a barcode reset reference sub-image, and then multiple barcode reset reference sub-images are obtained in the same way.

[0020] In conjunction with the first aspect, in certain implementations of the first aspect, determining the reference contrast corresponding to the barcode reset reference sub-image specifically includes:

[0021] Obtain an enhanced image of the barcode;

[0022] Obtain the edge feature points of the barcode reset reference sub-image, and obtain the edge feature points of the barcode enhancement image;

[0023] Based on the edge feature points of the barcode reset reference sub-image and the edge feature points of the barcode enhancement image, the reference repetition area of ​​the barcode reset reference sub-image is determined;

[0024] Based on the reference repeating region, determine the reference contrast corresponding to the barcode reset reference sub-image.

[0025] In conjunction with the first aspect, in certain implementations of the first aspect, determining the reference orientation factor corresponding to the barcode reset reference sub-image specifically includes:

[0026] Obtain an enhanced image of the barcode;

[0027] Obtain the centroid coordinates of the barcode reset reference sub-image, and obtain the centroid coordinates of the barcode enhancement image;

[0028] Based on the centroid coordinates of the barcode reset reference sub-image and the centroid coordinates of the barcode enhancement image, the reference orientation factor corresponding to the barcode reset reference sub-image is determined.

[0029] In conjunction with the first aspect, in certain implementations of the first aspect, determining the reset rotation features corresponding to each barcode reset reference sub-image specifically includes:

[0030] For any barcode reset reference sub-image, determine the edge feature points of the barcode reset reference sub-image, and determine the reset rotation feature of the barcode reset reference sub-image based on the edge feature points of the barcode reset reference sub-image;

[0031] Then, the same steps are used to determine the reset rotation features corresponding to each barcode reset reference sub-image.

[0032] In conjunction with the first aspect, in some implementations of the first aspect, a high-definition camera is used to capture a regional image of the area where the barcode is located to obtain the barcode captured image.

[0033] Secondly, this application provides a logistics tracking system, which adopts a cloud-edge collaborative architecture, including a logistics tracking edge device and a cloud. The logistics tracking edge device includes a logistics tracking barcode reset detection unit, which comprises:

[0034] The barcode acquisition module is used to initiate the logistics tracking barcode reset detection and acquire barcode images.

[0035] The processing module is used to perform edge detection and filtering on the barcode acquisition image to obtain an enhanced barcode image;

[0036] The processing module is also used to perform barcode reference separation on the barcode acquisition image to obtain multiple barcode reset reference sub-images;

[0037] The processing module is further configured to, for any barcode reset reference sub-image, determine the reference contrast and reference orientation factor corresponding to the barcode reset reference sub-image, and then determine the reference confidence weight corresponding to the barcode reset reference sub-image based on the reference contrast and reference orientation factor corresponding to the barcode reset reference sub-image.

[0038] The processing module is further configured to determine the reset rotation features corresponding to each barcode reset reference sub-image, and perform reset fusion on the reset rotation features corresponding to each barcode reset reference sub-image according to the reference confidence weights corresponding to each barcode reset reference sub-image to obtain the reference reset angle of the barcode;

[0039] The reset detection module is used to reset and detect the barcode enhancement image according to the reference reset angle, and send the detection result to the cloud.

[0040] Thirdly, this application provides a computer terminal device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described reset detection method for logistics tracking barcodes.

[0041] Fourthly, this application provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to implement the operations performed by the above-described method for resetting and detecting logistics tracking barcodes.

[0042] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0043] In the logistics tracking system and method provided in this application, the logistics tracking edge device first initiates barcode reset detection to acquire a barcode image; edge detection and filtering are performed on the barcode image to obtain an enhanced barcode image; barcode reference separation is performed on the acquired barcode image to obtain multiple barcode reset reference sub-images, each sub-image containing the barcode and a separate layer of reference background, providing different reference angles and contrasts; for any barcode reset reference sub-image, the reference contrast and reference orientation factor corresponding to that barcode reset reference sub-image are determined, and then the reference confidence weight corresponding to that barcode reset reference sub-image is determined based on the reference contrast and reference orientation factor corresponding to that barcode reset reference sub-image. The reference confidence weight quantifies the influence of the contrast and orientation factor of the reference in each sub-image on the position and angle of the barcode, thereby accurately determining the position and angle of the barcode. The actual rotation state of the barcode is determined; the reset rotation features corresponding to each barcode reset reference sub-image are determined, and the reset rotation features corresponding to each barcode reset reference sub-image are reset and fused according to the reference confidence weights of each barcode reset reference sub-image to obtain the reference reset angle of the barcode, thereby reducing the error that may be caused by a single reference object. Even when the reference object to which the barcode is attached is distorted, the actual angle of the barcode can still be accurately determined; finally, the barcode enhancement image is reset and detected according to the reference reset angle, and the detection results are sent to the cloud. In summary, this application determines the reference confidence weights of multiple reference objects to which the logistics tracking barcode is attached, and performs reset detection on the barcode enhancement image according to the image features of the reference objects. This enables accurate reset of the logistics tracking barcode when the reference object to which the logistics tracking barcode is attached is distorted, thereby improving the recognition rate of the logistics tracking barcode. Attached Figure Description

[0044] Figure 1 This is an exemplary flowchart of a method for resetting and detecting a logistics tracking barcode, according to some embodiments of this application.

[0045] Figure 2 This is a schematic diagram of the structure of a logistics tracking barcode reset detection unit according to some embodiments of this application;

[0046] Figure 3 This is a schematic diagram of the structure of a computer terminal device that implements a reset detection method for logistics tracking barcodes according to some embodiments of this application. Detailed Implementation

[0047] This application initiates barcode reset detection via a logistics tracking edge device to acquire a barcode image; performs edge detection and filtering on the acquired barcode image to obtain an enhanced barcode image; performs barcode reference separation on the acquired barcode image to obtain multiple barcode reset reference sub-images; for any given barcode reset reference sub-image, determines the corresponding reference contrast and reference orientation factor, and then determines the corresponding reference confidence weight based on the reference contrast and reference orientation factor; and determines the reset reference weight for each barcode. The reset rotation features corresponding to each sub-image are used to perform reset fusion based on the reference confidence weights corresponding to each barcode reset reference sub-image to obtain the reference reset angle of the barcode. Finally, the barcode enhancement image is reset and detected based on the reference reset angle. This application determines the reference confidence weights of the reference objects to which the barcode is attached and performs reset detection on the barcode enhancement image based on the image features of the reference objects. This enables accurate reset of logistics tracking barcodes when the reference objects to which the logistics tracking barcodes are attached are distorted, thereby improving the recognition rate of logistics tracking barcodes.

[0048] To better understand the above technical solutions, a detailed description of the solutions will be provided below in conjunction with the accompanying drawings and specific implementation methods. (Reference) Figure 1 The figure is an exemplary flowchart of a reset detection method for a logistics tracking barcode according to some embodiments of this application. The reset detection method 100 for a logistics tracking barcode mainly includes the following steps:

[0049] In step S101, the logistics tracking edge device initiates the logistics tracking barcode reset detection and acquires the barcode image.

[0050] Optionally, the logistics tracking edge device in this application can be, for example, a barcode scanning device or other edge devices. In some embodiments, the barcode acquisition image is a regional image of the area where the barcode is located. In specific implementation, a high-definition camera can be used to acquire a regional image of the area where the barcode is located to obtain the barcode acquisition image. Other devices or equipment capable of image acquisition can also be used, which are not limited here.

[0051] In step S102, edge detection and filtering are performed on the barcode acquisition image to obtain an enhanced barcode image.

[0052] It should be noted that the enhanced barcode image is the barcode feature image obtained by segmenting and filtering the acquired barcode image. Preferably, in some embodiments, edge detection and filtering of the acquired barcode image to obtain the enhanced barcode image can be performed in the following manner:

[0053] The captured barcode image is subjected to grayscale binarization to obtain a black and white image of the barcode;

[0054] The outline of the barcode black and white image is extracted to obtain the barcode outline features. Based on the barcode outline features, the acquired barcode image is segmented and filtered to obtain an enhanced barcode image.

[0055] In specific implementation, the step of performing grayscale binarization processing on the barcode acquisition image firstly converts the color image into a grayscale image, that is, simplifies the color information of the original image into a single-channel grayscale value. In some embodiments of this application, this is achieved by weighted averaging of the values ​​of the three RGB channels. Subsequently, the grayscale value of each pixel in the image is compared with a threshold. Pixels greater than the threshold are assigned white (255), and pixels less than the threshold are assigned black (0), thereby obtaining a binarized image. Then, the findContours contour detection algorithm can be used to extract the contour of the black and white barcode image to obtain multiple barcode contour features. Then, the found contours are filtered, and the barcode contour feature with the most similar shape to the barcode is selected. Then, a bounding box is drawn based on the selected barcode contour feature, which is used as the position of the barcode area. Finally, the original image is cropped based on the drawn bounding box to obtain the barcode enhanced image.

[0056] Optionally, in some embodiments, Gaussian filtering, a commonly used image filtering method, can be used to filter the barcode enhancement image. Gaussian filtering is a commonly used image smoothing method that can effectively remove noise from the image, making the image smoother and thus enhancing the clarity of the barcode. In specific implementations, other image filtering methods such as bilateral filtering can also be used, and this application does not limit them.

[0057] In step S103, the barcode acquisition image is subjected to barcode reference separation to obtain multiple barcode reset reference sub-images.

[0058] It should be noted that the barcode reset reference sub-image is a surface image of multiple reference objects to which the barcode is attached. For example, the mailbag containing the barcode is the first reference object, and the desk containing the mailbag is the second reference object. Since the reference object containing the barcode is usually a rectangular object with a smooth surface, in this application, barcode reference separation can be performed based on the rectangularity of the edge contour of the background reference object to obtain multiple barcode reset reference sub-images. In some embodiments, performing barcode reference separation on the barcode acquisition image to obtain multiple barcode reset reference sub-images specifically includes:

[0059] The barcode image is subjected to reference background recognition to obtain multiple reference background edge points;

[0060] Multiple reference background edge groups are obtained by grouping the points based on their positional relationships.

[0061] For any reference background edge group, background segmentation is performed based on all reference background edge points within the reference background edge group to obtain a barcode reset reference sub-image, and then multiple barcode reset reference sub-images are obtained in the same way.

[0062] Optionally, in some embodiments, the Canny edge detection algorithm can be used to identify the reference background in the barcode acquisition image to obtain multiple reference background edge points.

[0063] Optionally, in some embodiments, grouping multiple reference background edge points according to their positional relationships to obtain multiple reference background edge groups specifically includes:

[0064] Contour detection is performed on multiple reference background edge points to obtain multiple background contours. In specific implementation, contour detection can be performed using functions such as OpenCV's findContours.

[0065] Based on the positional relationship of the background contour, the reference background edge points in each background contour are grouped to obtain multiple reference background edge groups.

[0066] In practical implementation, each contour can first be fitted with a straight line using methods such as Hough Transform or Least Squares to fit the contour into straight line segments. Then, each straight line segment can be parameterized, for example, using slope and intercept (or rho and theta in polar coordinates) to describe each line. Based on the line parameters (such as slope, intercept, angle, etc.), they can be grouped. Grouping can typically be based on the following criteria: 1. Lines with the same or similar slopes can be considered parallel and belong to the same group; 2. Lines with a distance between them within a certain range can be grouped together, representing opposite sides of the same rectangle; 3. Detecting intersections between different lines can further confirm which lines constitute the sides of the rectangle. Each group of parallel lines is analyzed, first identifying pairs of lines that form rectangles; then, by checking pairwise intersections and distance relationships between lines, the existing rectangular boundaries can be found. Finally, all reference background edge points included within the rectangular boundaries are grouped as a reference background edge group.

[0067] In step S104, for any barcode reset reference sub-image, the reference contrast and reference orientation factor corresponding to the barcode reset reference sub-image are determined, and then the reference confidence weight corresponding to the barcode reset reference sub-image is determined based on the reference contrast and reference orientation factor corresponding to the barcode reset reference sub-image.

[0068] It should be noted that the reference contrast is the coverage of the barcode on the reference object corresponding to the barcode reset reference sub-image. In some embodiments, determining the reference contrast corresponding to the barcode reset reference sub-image specifically includes:

[0069] Obtain an enhanced image of the barcode;

[0070] Obtain the edge feature points of the barcode reset reference sub-image, and obtain the edge feature points of the barcode enhancement image;

[0071] Based on the edge feature points of the barcode reset reference sub-image and the edge feature points of the barcode enhancement image, the reference repetition area of ​​the barcode reset reference sub-image is determined;

[0072] Based on the reference repeating region, determine the reference contrast corresponding to the barcode reset reference sub-image.

[0073] In practice, the area of ​​the reference repeating region can be used as the proportion of the barcode reset reference sub-image as the reference contrast.

[0074] It should be noted that the reference orientation factor is the degree of center orientation deviation between the barcode and the reference object corresponding to the barcode reset reference sub-image. Optionally, in some embodiments, determining the reference orientation factor corresponding to the barcode reset reference sub-image specifically includes:

[0075] Obtain an enhanced image of the barcode;

[0076] Obtain the centroid coordinates of the barcode reset reference sub-image, and obtain the centroid coordinates of the barcode enhancement image;

[0077] Based on the centroid coordinates of the barcode reset reference sub-image and the centroid coordinates of the barcode enhancement image, the reference orientation factor corresponding to the barcode reset reference sub-image is determined.

[0078] Optionally, in some embodiments, the centroid coordinates of each barcode reset reference sub-image and the centroid coordinates of the barcode enhancement image can be differentiated and normalized respectively, and the normalization result corresponding to each barcode reset reference sub-image can be used as the reference orientation factor.

[0079] It should be noted that the reference confidence weight is used to reflect the reference confidence level of the barcode reference object corresponding to the barcode reset reference sub-image. Optionally, in some embodiments, the reference confidence weight corresponding to the barcode reset reference sub-image is the product between the reference contrast and the reference orientation factor corresponding to the barcode reset reference sub-image.

[0080] In step S105, the reset rotation features corresponding to each barcode reset reference sub-image are determined, and the reset rotation features corresponding to each barcode reset reference sub-image are reset fused according to the reference confidence weights corresponding to each barcode reset reference sub-image to obtain the reference reset angle of the barcode.

[0081] In some embodiments, determining the reset rotation features corresponding to each barcode reset reference sub-image can be achieved in the following manner:

[0082] For any barcode reset reference sub-image, determine the edge feature points of the barcode reset reference sub-image, and determine the reset rotation feature of the barcode reset reference sub-image based on the edge feature points of the barcode reset reference sub-image;

[0083] The same steps are used to determine the reset rotation features corresponding to each barcode reset reference sub-image.

[0084] Optionally, in some embodiments, determining the reset rotation feature of the barcode reset reference sub-image based on its edge feature points can be achieved in the following ways:

[0085] Based on the edge feature points of the barcode reset reference sub-image, the reference contour is extracted, and a straight line is fitted to the reference contour to obtain the slope of the edge line of the reference contour.

[0086] A barcode enhancement image is acquired, its contour is extracted, and a straight line is fitted to obtain the barcode edge slope.

[0087] The reset rotation feature of the barcode reset reference sub-image is obtained by differentiating the slope of the barcode edge and the slope of the edge line of the reference contour.

[0088] Optionally, in some embodiments, the slope difference between the slope of the barcode edge and the slope of the edge line of the reference contour can be transformed by arctangent, and the resulting function return value can be used as the reset rotation feature of the barcode reset reference sub-image.

[0089] It should be noted that the reference reset angle is a barcode reference angle obtained based on the contours of multiple background reference objects of the barcode. Resetting and repairing the barcode enhancement image based on the reference reset angle can improve the reading speed of the barcode in complex background environments. Optionally, in some embodiments, for example, the reference reset angle can be obtained by weighted fusion of the reset rotation features corresponding to each barcode reset reference sub-image based on the reference confidence weight corresponding to each barcode reset reference sub-image.

[0090] In step S106, the barcode enhancement image is reset and detected according to the reference reset angle, and the detection result is sent to the cloud.

[0091] Optionally, in some embodiments, the barcode enhancement image can be rotated accordingly based on the reference reset angle to reset and repair the barcode enhancement image, thereby improving the barcode reading speed in complex background environments.

[0092] Optionally, in some embodiments, barcode detection can be achieved by comparing the reset enhanced barcode image with images in a barcode recognition library.

[0093] Furthermore, in another aspect of this application, in some embodiments, this application provides a logistics tracking system that adopts a cloud-edge collaborative architecture. This cloud-edge architecture includes a logistics tracking edge device and a cloud, wherein the logistics tracking edge device includes a logistics tracking barcode reset detection unit. (Refer to...) Figure 2 The figure is a schematic diagram of the structure of a logistics tracking barcode reset detection unit according to some embodiments of this application. The logistics tracking barcode reset detection unit 200 includes: a barcode acquisition module 201, a processing module 202, and a reset detection module 203, which are described below:

[0094] In some specific embodiments of this application, the barcode acquisition module 201 is mainly used to initiate the logistics tracking barcode reset detection and acquire the barcode acquisition image;

[0095] In some specific embodiments of this application, the processing module 202 is used to perform edge detection and filtering on the barcode acquisition image to obtain an enhanced barcode image;

[0096] It should be noted that the processing module 202 described in this application is also used to perform barcode reference separation on the barcode acquisition image to obtain multiple barcode reset reference sub-images;

[0097] In addition, the processing module 202 described in this application is also used to determine the reference contrast and reference orientation factor corresponding to any barcode reset reference sub-image, and then determine the reference confidence weight corresponding to the barcode reset reference sub-image based on the reference contrast and reference orientation factor corresponding to the barcode reset reference sub-image.

[0098] In addition, the processing module 202 is also used to determine the reset rotation features corresponding to each barcode reset reference sub-image, and to perform reset fusion on the reset rotation features corresponding to each barcode reset reference sub-image according to the reference confidence weights corresponding to each barcode reset reference sub-image to obtain the reference reset angle of the barcode;

[0099] In some specific embodiments of this application, the reset detection module 203 is mainly used to reset and detect the barcode enhancement image according to the reference reset angle, and send the detection result to the cloud.

[0100] The foregoing detailed an example of the reset detection method for logistics tracking barcodes provided in the embodiments of this application. It is understood that the corresponding device includes hardware structures and / or software modules for performing each function in order to achieve the above functions.

[0101] Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a certain function in the application is executed in a manner that drives hardware or computer software depends on the specific application and design constraints of the technical solution. Therefore, those skilled in the art can use different methods to implement the described function for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0102] In addition, this application also provides a computer terminal device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described reset detection method for logistics tracking barcodes.

[0103] In some embodiments, reference Figure 3 The figure is a schematic diagram of the structure of a computer terminal device implementing a reset detection method for logistics tracking barcodes according to some embodiments of this application. The reset detection method for logistics tracking barcodes in the above embodiments can be achieved through... Figure 3 The computer terminal device 300 shown is used to implement this, and the computer terminal device 300 includes at least one communication bus 301, communication interface 302, processor 303 and memory 304.

[0104] The processor 303 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more devices for controlling the execution of the reset detection method for the logistics tracking barcode in this application.

[0105] The communication bus 301 may include a path for transmitting information between the aforementioned components.

[0106] Memory 304 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 304 may exist independently and be connected to processor 303 via communication bus 301. Memory 304 may also be integrated with processor 303.

[0107] The memory 304 stores program code for executing the scheme of this application, and its execution is controlled by the processor 303. The processor 303 executes the program code stored in the memory 304. The program code may include one or more software modules. In the above embodiments, the determination of the reference confidence weight can be implemented by the processor 303 and one or more software modules in the program code in the memory 304.

[0108] Communication interface 302 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0109] Optionally, the computer terminal device 300 may also include a power supply 305 for providing power to various devices or circuits in the real-time computer terminal device.

[0110] In a specific implementation, as one example, a computer terminal device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0111] The aforementioned computer terminal device can be a general-purpose computer terminal device or a dedicated computer terminal device. In specific implementations, the computer terminal device can be a desktop computer, a portable computer, a network server, a handheld computer (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer terminal device.

[0112] In addition, other aspects of this application provide a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to implement the operations performed by the above-described logistics tracking barcode reset detection method.

[0113] In summary, the logistics tracking system and method disclosed in this application first initiates barcode reset detection to acquire a barcode image; edge detection and filtering are performed on the acquired barcode image to obtain an enhanced barcode image; barcode reference separation is performed on the acquired barcode image to obtain multiple barcode reset reference sub-images; for any given barcode reset reference sub-image, the reference contrast and reference orientation factor corresponding to that sub-image are determined, and then the reference confidence weight corresponding to that sub-image is determined based on the reference contrast and reference orientation factor; and the weight of each... The reset rotation features corresponding to each barcode reset reference sub-image are used to perform reset fusion based on the reference confidence weight of each barcode reset reference sub-image to obtain the reference reset angle of the barcode. Finally, the barcode enhancement image is reset and detected based on the reference reset angle. This application determines the reference confidence weight of the reference object to which the barcode is attached and performs reset detection on the barcode enhancement image based on the image features of the reference object. This enables accurate reset of the logistics tracking barcode when the reference object to which the logistics tracking barcode is attached is distorted, thereby improving the recognition rate of the logistics tracking barcode.

[0114] The above descriptions are merely embodiments of this application, and common knowledge such as specific technical solutions or characteristics in the solutions are not described in detail here. It should be noted that those skilled in the art can make several modifications and improvements without departing from the technical solutions of this application, and these should also be considered within the scope of protection of this application, without affecting the effectiveness of the implementation of this application or the practicality of the patent.

[0115] The scope of protection claimed in this application shall be determined by the content of its claims. The specific embodiments described in the specification can be used to interpret the content of the claims. Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of the invention. Therefore, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for resetting and detecting logistics tracking barcodes, applied to a logistics tracking system, wherein the logistics tracking system adopts a cloud-edge collaborative architecture, the cloud-edge collaborative architecture including logistics tracking edge devices and a cloud, characterized in that, The method comprises: The logistics tracking edge device starts the logistics tracking barcode reset detection, and acquires a barcode collection image; Edge detection and filtering are performed on the barcode collection image to obtain a barcode enhanced image; Barcode reference separation is performed on the barcode collection image to obtain a plurality of barcode reset reference subgraphs; For any one barcode reset reference subgraph, the reference contrast and the reference orientation factor corresponding to the barcode reset reference subgraph are determined, and then the reference confidence weight corresponding to the barcode reset reference subgraph is determined according to the reference contrast and the reference orientation factor corresponding to the barcode reset reference subgraph; The reset rotation features corresponding to each barcode reset reference subgraph are determined, and the reset rotation features corresponding to each barcode reset reference subgraph are fused according to the reference confidence weight corresponding to each barcode reset reference subgraph to obtain a reference reset angle of the barcode; The barcode enhanced image is reset and detected according to the reference reset angle, and the detection result is sent to the cloud; Wherein, the barcode reference separation on the barcode collection image to obtain a plurality of barcode reset reference subgraphs specifically comprises: Reference background recognition is performed on the barcode collection image to obtain a plurality of reference background edge points; Grouping is performed according to the positional relationship of the plurality of reference background edge points to obtain a plurality of reference background edge groups; For any one reference background edge group, background segmentation is performed according to all reference background edge points in the reference background edge group to obtain one barcode reset reference subgraph, and then a plurality of barcode reset reference subgraphs are obtained in the same way; Wherein, the determination of the reference contrast corresponding to the barcode reset reference subgraph specifically comprises: The barcode enhanced image is acquired; The edge feature points of the barcode reset reference subgraph and the edge feature points of the barcode enhanced image are acquired; According to the edge feature points of the barcode reset reference subgraph and the edge feature points of the barcode enhanced image, the reference repeated area of the barcode reset reference subgraph is determined; According to the reference repeated area, the reference contrast corresponding to the barcode reset reference subgraph is determined; Wherein, the determination of the reference orientation factor corresponding to the barcode reset reference subgraph specifically comprises: The barcode enhanced image is acquired; The centroid coordinates of the barcode reset reference subgraph and the centroid coordinates of the barcode enhanced image are acquired; According to the centroid coordinates of the barcode reset reference subgraph and the centroid coordinates of the barcode enhanced image, the reference orientation factor corresponding to the barcode reset reference subgraph is determined.

2. The method of claim 1, wherein, The edge detection and filtering on the barcode collection image to obtain the barcode enhanced image specifically comprises: The barcode collection image is subjected to gray scale binaryzation processing to obtain a barcode black and white image; The barcode black and white image is subjected to contour extraction to obtain barcode contour features, and the barcode collection image is subjected to region segmentation and filtering according to the barcode contour features to obtain the barcode enhanced image.

3. The method of claim 1, wherein, The determination of the reset rotation features corresponding to each barcode reset reference subgraph specifically comprises: For any one barcode reset reference subgraph, the edge feature point of the barcode reset reference subgraph is determined, and the reset rotation feature of the barcode reset reference subgraph is determined according to the edge feature point of the barcode reset reference subgraph. Further, the corresponding reset rotation features of each barcode reset reference subgraph are determined by using the same steps.

4. The method of claim 1, wherein, The high-definition camera is used to collect the area image of the area where the barcode is located, and the barcode collection image is obtained.

5. A logistics tracking system employing the method of any one of claims 1 to 4 for reset detection of a logistics tracking barcode, the logistics tracking system employing a cloud-edge collaborative architecture comprising a logistics tracking edge device and a cloud, wherein the logistics tracking edge device comprises a logistics tracking barcode reset detection unit, characterized in that, The logistics tracking barcode reset detection unit comprises: The barcode collection module is used to start the logistics tracking barcode reset detection, and the barcode collection image is obtained. The processing module is used to perform edge detection and filtering on the barcode collection image to obtain a barcode enhanced image. The processing module is also used to perform barcode reference separation on the barcode collection image to obtain a plurality of barcode reset reference subgraphs. The processing module is also used to determine the reference contrast and reference orientation factor corresponding to each barcode reset reference subgraph, and further determine the reference confidence weight corresponding to each barcode reset reference subgraph according to the reference contrast and reference orientation factor corresponding to each barcode reset reference subgraph. The processing module is also used to determine the reset rotation feature corresponding to each barcode reset reference subgraph, and perform reset fusion on the reset rotation feature corresponding to each barcode reset reference subgraph according to the reference confidence weight corresponding to each barcode reset reference subgraph to obtain the reference reset angle of the barcode. The reset detection module is used to reset and detect the barcode enhanced image according to the reference reset angle, and send the detection result to the cloud.

6. A computer terminal device, characterized by The computer terminal device comprises a memory and a processor, the memory stores a code, the processor is configured to obtain the code, and perform the reset detection method of the logistics tracking barcode as claimed in any one of claims 1 to 4.

7. A computer-readable storage medium storing at least one computer program, characterized in that, The computer program is loaded and executed by the processor to realize the operations performed by the reset detection method of the logistics tracking barcode as claimed in any one of claims 1 to 4.

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