Control method, device and equipment of bar code recognition platform and medium

Abstract

The invention relates to a bar code recognition platform control method and device, equipment and a medium, and the method comprises the steps: collecting a current image, and recognizing the current image features of the current image; integrating the reference images, and identifying reference image features of the reference images; wherein the current image and the reference image are two adjacent images acquired successively; judging whether the current image features are matched with the reference image features or not; if yes, determining a relative target moving distance between the current image and the reference image; and if the target moving distance is greater than or equal to the preset distance threshold, controlling the bar code recognition platform to enter a preset automatic sensing mode. The accuracy of the bar code recognition platform entering the automatic induction mode can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of image processing technology, and in particular to a control method, device, equipment, and medium for a barcode recognition platform. Background Art

[0002] Automatic sensing mode is a common feature of barcode recognition platforms, widely used in retail settings. When no products are placed in front of the platform, it enters a dormant state, keeping the fill light off to reduce energy consumption. When the platform detects an approaching product, it automatically illuminates the fill light to improve the success rate of barcode recognition.

[0003] Currently, the automatic sensing solution for barcode recognition platforms on the market maintains the same camera exposure settings and compares the grayscale changes between two consecutive captured frames. This can easily lead to the automatic sensing mode being mistakenly activated when the ambient brightness changes suddenly or even when a slight brightness change is detected. Summary of the Invention

[0004] In order to solve the above technical problems, the present disclosure provides a control method, device, equipment and medium for a barcode recognition platform.

[0005] According to one aspect of the present disclosure, a method for controlling a barcode recognition platform is provided, comprising:

[0006] Acquire a current image and identify current image features of the current image;

[0007] Acquire a reference image and identify reference image features of the reference image; wherein the current image and the reference image are two adjacent images acquired successively;

[0008] Determining whether the current image feature matches the reference image feature;

[0009] If they match, determining the relative target movement distance between the current image and the reference image;

[0010] If the target moving distance is greater than or equal to a preset distance threshold, the barcode recognition platform is controlled to enter a preset automatic sensing mode.

[0011] According to another aspect of the present disclosure, a control device for a barcode recognition platform is provided, comprising:

[0012] a first image recognition module, configured to capture a current image and recognize current image features of the current image;

[0013] A second image recognition module is configured to capture a reference image and identify reference image features of the reference image; wherein the current image and the reference image are two adjacent images captured successively;

[0014] A feature judgment module, configured to judge whether the current image feature matches the reference image feature;

[0015] a distance determination module, configured to determine a relative target movement distance between the current image and the reference image if a match is found;

[0016] The control entry module is used to control the barcode recognition platform to enter a preset automatic sensing mode if the target moving distance is greater than or equal to a preset distance threshold.

[0017] According to another aspect of the present disclosure, an electronic device is provided, comprising:

[0018] processor;

[0019] a memory for storing instructions executable by the processor;

[0020] The processor is configured to read the executable instructions from the memory and execute the instructions to implement the above method.

[0021] According to another aspect of the present disclosure, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the above method.

[0022] The technical solution provided by the embodiments of the present disclosure has the following advantages over the prior art:

[0023] The technical solution provided by the embodiment of the present disclosure includes: capturing a current image and identifying the current image features of the current image; collecting a reference image and identifying the reference image features of the reference image; wherein the current image and the reference image are two adjacent images captured successively; determining whether the current image features match the reference image features; if they do, determining the relative target movement distance between the current image and the reference image; if the target movement distance is greater than or equal to a preset distance threshold, controlling the barcode recognition platform to enter a preset automatic sensing mode. Conversely, if the current image features do not match the reference image features, or the target movement distance is less than the distance threshold, the barcode recognition platform maintains the sensing state unchanged.

[0024] This solution extracts and matches image features. If the two match and the target moves a significant distance, it indicates that the scanned object may have left the sensing area and then moved closer for a second scan, necessitating the automatic sensing mode. Therefore, once these conditions are met, this solution controls the barcode recognition platform to enter the preset automatic sensing mode. This solution relies solely on image features representing the local texture details of the image and is insensitive to changes in image brightness. It also imposes no strict requirements on camera exposure time or exposure gain. This significantly reduces the probability of the barcode recognition platform mistakenly entering automatic sensing mode, thereby increasing its accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0026] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0027] Figure 1 A flowchart of a control method for a barcode recognition platform provided in an embodiment of the present disclosure;

[0028] Figure 2 is a flow chart of another method for controlling a barcode recognition platform provided by an embodiment of the present disclosure;

[0029] Figure 3 It is a structural diagram of a control device of a barcode recognition platform provided by an embodiment of the present disclosure;

[0030] Figure 4 It is a structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features therein can be combined with each other in the absence of conflict.

[0032] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0033] At present, the automatic sensing solution of the barcode recognition platform only relies on the brightness change of adjacent frames of the image, and it is very easy to enter the automatic sensing mode by mistake. Based on this, in order to reduce the probability of the barcode recognition platform entering the automatic sensing mode by mistake to a greater extent, the embodiment of the present disclosure provides a control method, device, equipment and medium for a barcode recognition platform. The solution is used to implement the automatic sensing function of the barcode recognition platform by using a software method based on image matching while keeping the product hardware unchanged. This solution extracts image features from two consecutively captured images respectively, and controls the barcode recognition platform to enter the automatic sensing mode by matching the image features. This can reduce the probability of the barcode recognition platform entering the automatic sensing mode by mistake and improve the accuracy of the barcode recognition platform entering the automatic sensing mode.

[0034] For ease of understanding, the embodiments of the present disclosure are described below.

[0035] Figure 1 This is a flow chart of a control method for a barcode recognition platform provided by an embodiment of the present disclosure. This method can be executed by a control device of the barcode recognition platform, which can be implemented using software and / or hardware. Figure 1 As shown, a control method for a barcode recognition platform may include the following steps.

[0036] S102: Capture a current image and identify current image features of the current image.

[0037] S104: Acquire a reference image and identify reference image features of the reference image; wherein the current image and the reference image are two adjacent images acquired successively.

[0038] In this embodiment, the barcode recognition platform's camera can sequentially capture a current image A and the next adjacent image, i.e., a reference image B. After capturing current image A, a preset feature extraction model is used to identify current image features Fa of current image A. Furthermore, after capturing reference image B, a preset feature extraction model is used to identify reference image features Fb of reference image B.

[0039] The above-mentioned current image features and reference image features may include: features that characterize image detail texture, such as: local image grayscale information, image edge contour information, image corner points, image edge points, image local feature points, and a series of features that can characterize image detail texture.

[0040] Specifically, the current image features and the reference image features can each be composed of a set of feature point coordinates and a set of feature descriptors used to describe the features. The feature descriptors in the current image features and the feature descriptors in the reference image features can each include any of the following: a descriptor based on a gradient directional histogram, an LBP descriptor, an ORB descriptor, a contour encoding, and a descriptor generated based on a preset neural network. Of course, the above are only examples of feature descriptors, and this embodiment does not limit the types of feature descriptors.

[0041] S106: Determine whether the current image features match the reference image features.

[0042] In this embodiment, the process of determining whether the current image features match the reference image features may include the following contents.

[0043] Based on a preset matching algorithm, the degree of feature matching between the feature descriptors in the current image features and the feature descriptors in the reference image features is determined. The matching algorithm in this embodiment is an image feature matching algorithm based on similarity calculation between feature descriptors. For example, the matching algorithm may be a series of metric algorithms that can describe the similarity of feature descriptors, such as Euclidean distance, Street distance, Hamming distance, or cosine distance. Based on this matching algorithm, the similarity between the feature descriptors in the current image features and the feature descriptors in the reference image features is calculated, and the calculated similarity is used as the feature matching degree.

[0044] Determine whether the feature matching degree is greater than a preset matching degree threshold; if so, determine that the current image feature matches the reference image feature; and execute the subsequent step S108.

[0045] If not, it is determined that the current image features do not match the reference image features. In this embodiment, if the current image features do not match the reference image features, the reference image and its corresponding reference image features are used as the new current image and new current image features. Furthermore, the process returns to the step of capturing the reference image and identifying the reference image features of the reference image, i.e., returning to step S104. Specifically, the reference image is used as the current image, and its reference image features are used as the current image features. The next adjacent image, i.e., a new reference image, is captured, and the reference image features of the new reference image are identified.

[0046] S108: If they match, determine the relative target movement between the current image and the reference image.

[0047] This embodiment includes: determining, based on a preset matching algorithm, target feature points that are successfully matched between feature points in the current image features and feature points in the reference image features; establishing a mapping relationship between the current image and the reference image based on the target feature points; wherein the mapping relationship is used to represent the rotation angle of the current image relative to the reference image and the movement distance in at least one direction; and determining the relative target movement distance between the current image and the reference image based on the rotation angle and the movement distance in at least one direction.

[0048] Specifically, a preset matching algorithm can be used to determine target feature points that successfully match feature points in the current image's features with those in the reference image's features. A mapping relationship between current image A and reference image B can then be constructed based on these successfully matched target feature points. This mapping relationship can be represented by a set of 3x3 mapping matrices. This mapping matrix can be used to determine information such as the translation distance and rotation angle of current image A relative to reference image B. This translation distance can include translation distances in multiple directions, such as horizontal and vertical directions.

[0049] The relative target movement distance between the current image and the reference image is determined based on the above translation distance and rotation angle.

[0050] S110: If the target moving distance is greater than or equal to the preset distance threshold, the barcode recognition platform is controlled to enter a preset automatic sensing mode.

[0051] When the features of the current image and the reference image match, and the relative target movement distance is large (greater than or equal to the distance threshold), it means that the local texture details between the two adjacent images have not changed or have changed very slightly. At the same time, the two images have shifted significantly. This situation often occurs when the scanned object leaves and then approaches the barcode recognition platform's sensing area again. In this case, to facilitate the second automatic scan, the barcode recognition platform needs to enter the preset automatic sensing mode.

[0052] In another scenario, if the target moving distance is less than the distance threshold, the reference image and its corresponding reference image features are used as the new current image and new current image features; and, return to step S104, that is, return to the step of capturing the reference image and identifying the reference image features of the reference image.

[0053] Based on the above embodiments, an embodiment of the same barcode delay function applicable to a barcode recognition platform may also be provided.

[0054] Combine Figure 2 As shown, in this embodiment, a method for delayed reading of the same barcode based on image feature matching is provided, comprising:

[0055] S201, collecting the current image;

[0056] S202, determining whether the current image features of the current image are successfully recognized; if the recognition fails, returning to step S201;

[0057] S203, if the recognition is successful, the response is allowed and the current image features of the current image are obtained;

[0058] S204, collecting a reference image and identifying reference image features of the reference image;

[0059] S205, determining whether the current image features match the reference image features;

[0060] S206, if it matches, prohibit the response and return to step S204;

[0061] S207: If there is no match, the reference image is used as the new current image and the process returns to step S202.

[0062] In summary, the control method of the barcode recognition platform provided by the embodiment of the present disclosure includes: capturing a current image and identifying the current image features of the current image; collecting a reference image and identifying the reference image features of the reference image; wherein the current image and the reference image are two adjacent images captured successively; determining whether the current image features match the reference image features; if they do, determining the relative target movement distance between the current image and the reference image; if the target movement distance is greater than or equal to a preset distance threshold, controlling the barcode recognition platform to enter a preset automatic sensing mode. Conversely, if the current image features do not match the reference image features, or the target movement distance is less than the distance threshold, the barcode recognition platform maintains the sensing state unchanged.

[0063] This solution extracts and matches image features. If the two match and the target moves a significant distance, it indicates that the scanned object may have left the sensing area and then moved closer for a second scan, necessitating the automatic sensing mode. Therefore, once these conditions are met, this solution controls the barcode recognition platform to enter the preset automatic sensing mode. This solution relies solely on image features representing the local texture details of the image and is insensitive to changes in image brightness. It also imposes no strict requirements on camera exposure time or exposure gain. This significantly reduces the probability of the barcode recognition platform mistakenly entering automatic sensing mode, thereby increasing its accuracy.

[0064] In addition, this solution does not involve any improvement in product hardware and will not increase product costs. It can enter the automatic sensing mode more correctly and accurately through a pure software method.

[0065] Figure 3This is a schematic diagram of the structure of a control device for a barcode recognition platform provided by an embodiment of the present disclosure. The device can be used to implement the control method of the barcode recognition platform described above. The device can be implemented using software and / or hardware. Figure 3 As shown, a control device of a barcode recognition platform may include the following modules.

[0066] A first image recognition module 310 is configured to capture a current image and recognize current image features of the current image;

[0067] The second image recognition module 320 is configured to capture a reference image and identify reference image features of the reference image; wherein the current image and the reference image are two adjacent images captured successively;

[0068] A feature determination module 330 is configured to determine whether the current image feature matches the reference image feature;

[0069] a distance determination module 340 for determining a relative target movement distance between the current image and the reference image if a match is found;

[0070] The control entry module 350 is configured to control the barcode recognition platform to enter a preset automatic sensing mode if the target moving distance is greater than or equal to a preset distance threshold.

[0071] In one embodiment, the feature determination module 330 is further configured to:

[0072] Determining, according to a preset matching algorithm, a feature matching degree between a feature descriptor in the current image feature and a feature descriptor in the reference image feature;

[0073] Determining whether the feature matching degree is greater than a preset matching degree threshold;

[0074] If yes, determining that the current image feature matches the reference image feature;

[0075] If not, it is determined that the current image feature does not match the reference image feature.

[0076] The device provided in this embodiment has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0077] Figure 4 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. Figure 4 As shown, the electronic device 400 includes one or more processors 401 and a memory 402 .

[0078] The processor 401 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 400 to perform desired functions.

[0079] The memory 402 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 401 may execute the program instructions to implement the control method of the barcode recognition platform of the embodiment of the present disclosure described above and / or other desired functions. Various contents such as input signals, signal components, noise components, etc. may also be stored in the computer-readable storage medium.

[0080] In one example, the electronic device 400 may further include an input device 403 and an output device 404 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0081] In addition, the input device 403 may also include, for example, a keyboard, a mouse, and the like.

[0082] The output device 404 can output various information to the outside, including determined distance information, direction information, etc. The output device 404 can include, for example, a display, a speaker, a printer, a communication network and its connected remote output device, etc.

[0083] Of course, to simplify, Figure 4 Only some of the components related to the present disclosure in the electronic device 400 are shown, and components such as buses, input / output interfaces, etc. are omitted. In addition, the electronic device 400 may further include any other appropriate components according to specific application scenarios.

[0084] Furthermore, this embodiment also provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is used to execute the control method of the above-mentioned barcode recognition platform.

[0085] The embodiments of the present disclosure provide a computer program product for a control method, device, electronic device, and medium for a barcode recognition platform, including a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the previous method embodiments. For specific implementation, please refer to the method embodiments and will not be repeated here.

[0086] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0087] The foregoing description is intended only to provide specific embodiments of the present disclosure, intended to enable those skilled in the art to understand and implement the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments described herein, but rather to be construed in the broadest manner consistent with the principles and novel features disclosed herein.

Claims

1. A control method for a barcode recognition platform, characterized in that: include: Acquire a current image and identify current image features of the current image; Acquire a reference image and identify reference image features of the reference image; wherein the current image and the reference image are two adjacent images acquired successively; Determining whether the current image feature matches the reference image feature; If they match, determining the relative target movement distance between the current image and the reference image; If the target moving distance is greater than or equal to a preset distance threshold, the barcode recognition platform is controlled to enter a preset automatic sensing mode.

2. The method according to claim 1, characterized in that The determining whether the current image feature matches the reference image feature includes: Determining, according to a preset matching algorithm, a feature matching degree between a feature descriptor in the current image feature and a feature descriptor in the reference image feature; Determining whether the feature matching degree is greater than a preset matching degree threshold; If yes, determining that the current image feature matches the reference image feature; If not, it is determined that the current image feature does not match the reference image feature.

3. The method according to claim 1, characterized in that The determining of the relative target movement distance between the current image and the reference image includes: Determining target feature points that are successfully matched between feature points in the current image features and feature points in the reference image features according to a preset matching algorithm; Establishing a mapping relationship between the current image and the reference image based on the target feature points; wherein the mapping relationship is used to represent a rotation angle and a movement distance in at least one direction of the current image relative to the reference image; A relative target movement distance between the current image and the reference image is determined based on the rotation angle and the movement distance in the at least one direction.

4. The method according to claim 1, wherein The method further comprises: If the current image feature does not match the reference image feature, or if the target movement distance is less than the distance threshold, using the reference image and its corresponding reference image feature as a new current image and a new current image feature; Return to the step of acquiring a reference image and identifying reference image features of the reference image.

5. The method according to claim 1, wherein The current image features and the reference image features both include features that characterize image detail texture.

6. The method according to claim 1, characterized in that The feature descriptors in the current image features and the feature descriptors in the reference image features include any one of the following: a descriptor established based on a gradient direction histogram, an LBP descriptor, an ORB descriptor, a contour code, and a descriptor generated based on a preset neural network.

7. A control device for a barcode recognition platform, characterized in that: include: a first image recognition module, configured to capture a current image and recognize current image features of the current image; A second image recognition module is configured to capture a reference image and identify reference image features of the reference image; wherein the current image and the reference image are two adjacent images captured successively; A feature judgment module, configured to judge whether the current image feature matches the reference image feature; a distance determination module, configured to determine a relative target movement distance between the current image and the reference image if a match is found; The control entry module is used to control the barcode recognition platform to enter a preset automatic sensing mode if the target moving distance is greater than or equal to a preset distance threshold.

8. The device according to claim 7, characterized in that The feature judgment module is also used for: Determining, according to a preset matching algorithm, a feature matching degree between a feature descriptor in the current image feature and a feature descriptor in the reference image feature; Determining whether the feature matching degree is greater than a preset matching degree threshold; If yes, determining that the current image feature matches the reference image feature; If not, it is determined that the current image feature does not match the reference image feature.

9. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device implements the method according to any one of claims 1 to 6.

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