Method and device for identifying authenticity of article, and storage equipment

Abstract

The invention relates to the technical field of intelligent household appliances, and discloses a method for identifying authenticity of an article. The method comprises the steps of obtaining a detection image of a to-be-identified article in a normal light source; performing target detection on the detection image, and generating a corresponding recognition frame at the maximum contour of each detection target; wherein the detection target has appearance contour features of the to-be-identified article; when a region corresponding to the target detection at least has a first identification frame and a second identification frame and the first identification frame is located in the second identification frame, marking a frame selection region of the to-be-identified article corresponding to the first identification frame as a to-be-confirmed region; and obtaining a to-be-identified image of which the to-be-confirmed area is in the colorful light source, and determining the authenticity of the to-be-identified article according to the to-be-identified image. According to the method, the accuracy of an article identification result is improved by analyzing the intensity of the light presented by the to-be-confirmed area in the to-be-identified image of the colorful light source. The invention also discloses a device for identifying the authenticity of the article and storage equipment.

Description

Technical Field

[0001] The present application relates to the technical field of smart home appliances, for example, to a method, device, and storage device for identifying the authenticity of an object. Background Art

[0002] In the scenario of full-space storage recognition, there are some flat ingredients, such as yogurt in cartons, fruits in cartons, etc. When identifying such flat ingredients with outer packaging and printed food patterns on the outer packaging, the printed pattern on the outer packaging will be mistakenly recognized as the food.

[0003] In order to identify whether the items in the storage space are real objects or printed patterns, the relevant technology discloses an object recognition method. The recognition method collects the image of the object to be identified through a camera, performs target detection on the image, generates a target detection frame that corresponds one-to-one to the target in the image, obtains all the object information contained in the image by identifying the target detection frames one by one, and confirms whether the object to be identified is a real object based on all the object information contained in the image.

[0004] In the process of implementing the embodiments of the present disclosure, it was found that the related technology has at least the following problems: although the related technology can theoretically distinguish whether the object to be identified is a real object, in actual use, when the pattern printed on the outer packaging of the object is very realistic, the object image printed on the outer packaging of the object may be mistakenly regarded as a real object when performing target detection on the image, resulting in inaccurate object recognition results.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0007] The embodiments of the present disclosure provide a method, an apparatus, and a storage device for identifying the authenticity of an object, so as to improve the accuracy of the object identification result when the pattern printed on the outer packaging of the object to be identified is very realistic.

[0008] In some embodiments, a method for object identification includes: obtaining a detection image of an object to be identified under a normal light source; performing target detection on the detection image, and generating a corresponding identification frame at the maximum contour of each detection target; wherein the detection target has the appearance contour characteristics of the object to be identified; when the area corresponding to the target detection has at least a first identification frame and a second identification frame, and the first identification frame is within the second identification frame, marking the framed area of ​​the object to be identified corresponding to the first identification frame as a to-be-confirmed area; obtaining an image to be identified in which the to-be-confirmed area is under a colorful light source, and determining the authenticity of the object to be identified based on the image to be identified.

[0009] Optionally, obtaining an image to be identified in which the area to be confirmed is under a colorful light source includes: taking an image of the area to be confirmed under a normal light source as a control image; taking an image of the area to be confirmed under one or more colorful light sources as a test image; wherein a proportion of a specific color attribute of the colorful light source is greater than a proportion of a specific color attribute of the normal light source.

[0010] Optionally, determining the authenticity of the object to be identified based on the image to be identified includes: determining pixel comparison parameters of test pixels of the test image relative to control pixels of the control image; wherein the pixel comparison parameters include multiple Euclidean distances between the test pixels and the control pixels; and determining the authenticity of the object to be identified based on the pixel comparison parameters.

[0011] Optionally, the authenticity of the object to be identified is determined based on pixel comparison parameters, including: determining the average Euclidean distance between test pixels and control pixels of a test image corresponding to one or more colorful light sources; mapping the straight line corresponding to the average Euclidean distance to a two-dimensional coordinate system; determining a test slope value of the straight line corresponding to the average Euclidean distance, and determining the authenticity of the object to be identified based on the test slope value.

[0012] Optionally, the average Euclidean distance is calculated as follows: determine multiple Euclidean distances between multiple test pixels and corresponding multiple control pixels of the test image corresponding to each colorful light source; average the multiple Euclidean distances of the test image corresponding to each colorful light source to obtain the average Euclidean distance of each colorful light source.

[0013] Optionally, the authenticity of the object to be identified is determined based on the test slope value, including: when the test slope value satisfies a preset slope condition, determining that the object to be identified is a genuine object; wherein, when the test slope value corresponds to a test image corresponding to a colorful light source, the preset slope condition includes that the test slope value is not within a first slope value range corresponding to the colorful light source; and / or, when the test slope value corresponds to a test image corresponding to multiple colorful light sources, the preset slope condition includes that the number of test slope values ​​that are not within the second slope value range among multiple test slope values ​​is greater than a preset number.

[0014] Optionally, the method for object identification further includes: before performing target detection on the detection image, determining that the object to be identified is in a moving state within a preset time period.

[0015] Optionally, it is determined that the object to be identified is in a moving state within a preset time period in the following manner: within the preset time period, the pixel change rates of the object to be identified in a preset area are obtained in sequence; if the maximum pixel change rate among the pixel change rates is greater than the preset change rate, it is determined that the object to be identified is in a moving state within the preset time period.

[0016] In some embodiments, a device for identifying the authenticity of an item includes a processor and a memory storing program instructions, and the processor is configured to execute the above-mentioned method for identifying the authenticity of an item when running the program instructions.

[0017] In some embodiments, the storage device includes: a storage device body; a light source device, disposed on the storage device body, configured to provide a normal light source and a colorful light source; a shooting device, disposed on the storage device body, configured to shoot images of the object to be identified under the normal light source and the colorful light source; and, as described above, a device for identifying the authenticity of an object, installed on the storage device body.

[0018] The method, device, and storage device for object identification provided by the embodiments of the present disclosure can achieve the following technical effects:

[0019] In the disclosed embodiment, target detection is performed on the detection image of the normal light source to determine whether misidentification will occur when identifying the object to be identified. When the area corresponding to the target detection has at least a first identification frame and a second identification frame, and the first identification frame is within the second identification frame, it is determined that misidentification will occur when identifying the object to be identified, and the area to be identified is accurately identified. Since real objects and printed objects have different light absorption capabilities, the intensity of the reflected light is also different. Therefore, the authenticity of the object to be identified is accurately identified by analyzing the intensity of light presented in the area to be identified in the image to be identified by the colorful light source, thereby avoiding misidentification when the pattern of the printed object is very realistic, thereby improving the accuracy of the object identification results.

[0020] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0022] Figure 1 is a schematic diagram of the structure of a storage device provided by an embodiment of the present disclosure;

[0023] Figure 2 yes Figure 1 A schematic structural diagram of a light source device in a disclosed embodiment is shown;

[0024] Figure 3 is a schematic diagram of a method for identifying the authenticity of an object provided by an embodiment of the present disclosure;

[0025] Figure 4 is a schematic diagram of another method for identifying the authenticity of an object provided by an embodiment of the present disclosure;

[0026] Figure 5 is a schematic diagram of a device for identifying the authenticity of an object provided by an embodiment of the present disclosure;

[0027] Figure 6 It is a schematic diagram of a storage device product provided by an embodiment of the present disclosure.

[0028] Reference numerals:

[0029] 1. Storage equipment; 300. Items to be identified;

[0030] 10. Storage equipment body;

[0031] 20. Light source device; 201. Colorful light source; 2011. Red light source; 2012. Green light source; 2013. Blue light source; 2014. Yellow light source;

[0032] 30. Photographic device;

[0033] 200. Device for identifying the authenticity of an article; 500. Processor; 501. Memory; 502. Communication interface; 503. Bus. DETAILED DESCRIPTION

[0034] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0035] The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0036] Unless otherwise stated, the term "plurality" means two or more.

[0037] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0038] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0039] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0040] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0041] The present disclosure provides a storage device 1 such as Figure 1 As shown, the storage device 1 includes a storage device body 10, a light source device 20, a shooting device 30 and an electric control device (not shown in the figure). Among them, the light source device 20 is arranged on the storage device body 10, and is configured to provide a normal light source and a colorful light source. The shooting device 30 is arranged on the storage device body 10, and is configured to shoot images of the object 300 to be identified under normal light source and colorful light source. The electric control device includes a processor. The processor is used to control the working state of the light source device 20 and the shooting device 30, so as to realize various functions of the storage device 1.

[0042] Optionally, the photographing device 30 and the light source device 20 are both disposed on the top of the storage space in the storage device body 10 .

[0043] Optionally, the light source device 20 and the camera device 30 are arranged in a row. In this way, the illumination range of the light source device 20 on the object 300 to be identified can be expanded, thereby improving the accuracy of object identification. The camera device 30 is fixedly connected to the light source device 20 and is located at the center of the light source device 20.

[0044] Optionally, the light source device 20 includes a normal light source and a colorful light source 201 .

[0045] Among them, the normal light source includes one or more of the natural light source, flashlight, incandescent lamp, LED lamp and the light source provided by the storage device 1, but is not limited to this. The above-mentioned normal light source selection is only the preferred light source recommended for the convenience of implementing the technical solution of the present invention. Those skilled in the art can replace it with other multiple normal light sources according to the technical concept of the present invention to implement the technical solution of the present invention.

[0046] Optionally, a colorful light source 201 includes any one of a red light source, a green light source, a blue light source and a yellow light source.

[0047] Optionally, the multiple colorful light sources 201 include multiple types of red light sources, green light sources, blue light sources and yellow light sources. Figure 2 As shown, the multiple colorful light sources 201 include a red light source 2011, a green light source 2012, a blue light source 2013 and a yellow light source 2014. Since the above four tones are very different, the differences between the obtained test images are more obvious. At the same time, by setting up multiple colorful light sources 201, the intensity of light reflected by the object to be identified 300 under multiple colors can be analyzed, the identification deviation can be reduced, and it is more conducive to determining the authenticity of the object to be identified 300.

[0048] Optionally, combined Figure 2 As shown, a red light source 2011, a green light source 2012, a blue light source 2013 and a yellow light source 2014 are respectively arranged on both sides of the camera 30 in sequence from the first direction. For example, the red light source 2011 is respectively located at the farthest position on one side of the camera 30 and at the closest position on the other side of the camera 30. In this way, when a certain colorful light source 201 is turned on, the light received by the test image captured by the camera 30 can be made more uniform.

[0049] Combination Figure 1 and Figure 2 The storage device shown in the figure, the embodiment of the present disclosure provides a method for identifying the authenticity of an item. Figure 3 As shown, the method includes:

[0050] S301, the processor obtains a detection image of the object to be identified under normal light source.

[0051] S302: The processor performs target detection on the detection image and generates a corresponding recognition frame at the maximum contour of each detected target.

[0052] The detection target has the appearance contour feature of the object to be identified. The method of performing target detection on the detection image can refer to the existing image target detection algorithm. The method of generating a corresponding recognition frame at the maximum contour of each detection target can refer to the existing image classification and recognition algorithm.

[0053] S303: When the area corresponding to the target detection has at least a first identification frame and a second identification frame, and the first identification frame is within the second identification frame, the processor marks the framed area of ​​the to-be-identified object corresponding to the first identification frame as a to-be-confirmed area.

[0054] S304, the processor obtains an image to be identified in which the area to be confirmed is under a colorful light source, and determines the authenticity of the object to be identified based on the image to be identified.

[0055] In the above embodiment, target detection is performed on the detection image of the normal light source to determine whether misidentification will occur when identifying the object to be identified. When the area corresponding to the target detection has at least a first identification frame and a second identification frame, and the first identification frame is within the second identification frame, it is determined that misidentification will occur when identifying the object to be identified, and the area to be confirmed is accurately identified. Since real objects and printed objects have different light absorption capabilities, the intensity of the reflected light is also different. Therefore, the authenticity of the object to be identified is accurately identified by analyzing the intensity of light presented in the area to be confirmed in the image to be identified by the colorful light source, thereby avoiding misidentification when the pattern of the printed object is very realistic, thereby improving the accuracy of the object identification result.

[0056] Optionally, the processor obtains the image to be identified of the area to be confirmed under a colorful light source, including: the processor uses the image of the area to be confirmed under a normal light source as a control image. The processor uses the image of the area to be confirmed under one or more colorful light sources as a test image.

[0057] The specific color attribute ratio of the colorful light source is greater than that of the normal light source. For example, the specific color attribute ratios of the red light source, the green light source, the blue light source, and the yellow light source are all greater than that of the normal light source.

[0058] Exemplarily, the number of images under each colorful light source is [4, 8]. Preferably, the number of images under each colorful light source is 4. For example, when the multiple colorful light sources include a red light source, a green light source, a blue light source, and a yellow light source, there are 4 images under the red light source, the green light source, the blue light source, and the yellow light source, respectively, and there are 16 test images in total. In this way, taking multiple images under each colorful light source reduces errors, avoids contingency, and makes the recognition result more accurate.

[0059] For example, the number of control images is 1. Since the control image only serves to compare parameters with the test image, only 1 control image is required.

[0060] Optionally, the processor determining the authenticity of the object to be identified according to the image to be identified includes: the processor determining a pixel comparison parameter of a test pixel of the test image relative to a reference pixel of the reference image. The processor determines the authenticity of the object to be identified according to the pixel comparison parameter.

[0061] The pixel comparison parameter includes a plurality of Euclidean distances between the test pixel and the control pixel. The Euclidean distance represents the intensity of light reflected by the object to be identified. The larger the Euclidean distance, the greater the intensity of light reflected by the object to be identified. The smaller the Euclidean distance, the smaller the intensity of light reflected by the object to be identified.

[0062] Exemplarily, the control pixel points include all the pixel points in the control image. The test pixel points include all the pixel points in the test image, and the test pixel points correspond to the control pixel points one by one. In this way, the difference in the intensity of the light reflected by the object to be identified under the normal light source and the colorful light source can be more comprehensively known.

[0063] Exemplarily, the processor obtains the test pixel points corresponding to the control pixel points in the following manner: the processor obtains the two-dimensional coordinates corresponding to the i-th control pixel point in the control image. The processor determines the pixel point of the test image corresponding to the two-dimensional coordinates, and determines the pixel point as the test pixel point corresponding to the i-th control pixel point. Wherein, i=1, 2...n. n is the total number of all pixels in the control image, and i is the number of the pixel points in the control image. For example, the two-dimensional coordinates corresponding to the i-th control pixel point of the control image are (2, 3), then the two-dimensional coordinates (2, 3) of the test image and the corresponding pixel point are found, and the pixel point is determined as the test pixel point corresponding to the i-th control pixel point. By analogy, the test pixel points corresponding to the control pixel points can be obtained.

[0064] In this embodiment, the difference in the intensity of light reflected by the test image of the object to be identified under the colorful light source and the control image under the normal light source is determined by comparing multiple Euclidean distances between the pixel points and the corresponding test pixel points. The authenticity of the object to be identified can be accurately determined by the difference in the intensity of the reflected light.

[0065] Optionally, the processor determines the authenticity of the object to be identified based on the pixel comparison parameter, including: the processor determines the average Euclidean distance between the test pixel points and the control pixel points of the test image corresponding to one or more colorful light sources. The processor maps the straight line corresponding to the average Euclidean distance to a two-dimensional coordinate system. The processor determines the test slope value of the straight line corresponding to the average Euclidean distance, and determines the authenticity of the object to be identified based on the test slope value.

[0066] In the above embodiment, the average Euclidean distance is intuitively represented by the test slope value, thereby presenting the difference in the intensity of light reflected by the object to be identified under the normal light source and the colorful light source. Therefore, the true situation of the object to be identified can be accurately obtained through the test slope value.

[0067] Optionally, the processor calculates the average Euclidean distance in the following manner: the processor determines a plurality of Euclidean distances between a plurality of test pixels of the test image corresponding to each colorful light source and a plurality of corresponding control pixels. The processor averages the plurality of Euclidean distances of the test image corresponding to each colorful light source to obtain the average Euclidean distance of each colorful light source.

[0068] In the above embodiment, since each colorful light source includes Euclidean distances between multiple reference pixel points and corresponding test pixel points, the average value of multiple Euclidean distances is calculated to obtain a more comprehensive and overall difference in the intensity of the reflected light of the object to be identified in the reference image and the test image, thereby making the recognition result more accurate.

[0069] Optionally, the processor determines the authenticity of the object to be identified according to the test slope value, including: when the test slope value satisfies a preset slope condition, the processor determines that the object to be identified is a real object.

[0070] Wherein, when the test slope value corresponds to a test image corresponding to one colorful light source, the preset slope condition includes that the test slope value is not within the range of the first slope value corresponding to the colorful light source. And / or, when the test slope value corresponds to the test images corresponding to multiple colorful light sources, the preset slope condition includes that the number of test slope values ​​that are not within the second slope value range among the multiple test slope values ​​is greater than the preset number.

[0071] It should be noted that, due to different packaging materials and different lighting colors, the set first slope value is also different.

[0072] Optionally, the processor obtains the first slope value in the following manner: for common packaging materials on the market, such as paper, plastic box, and iron box, respectively obtain control images of the paper, plastic box, and iron box under normal light source, and respectively obtain test images of the paper, plastic box, and iron box under the colorful light source.

[0073] Among them, the control image of the paper under normal light source is marked as C 1 , the control image of the plastic box under normal light source is marked as C 2 , the control image of the iron box under normal light source is marked as C 3 The test image of the paper under this colorful light source is marked as S 1 The test image of the plastic box under this colorful light source is marked as S 2The test image of the iron box under this colorful light source is marked as S 3 .

[0074] Calculate C 1 All pixels in S 1 The average Euclidean distance A of all corresponding pixels in 1 , and calculate A 1 The slope value of the straight line is B 1 Calculate C 2 All pixels in S 2 The average Euclidean distance A of all corresponding pixels in 2 , and calculate A 2 The slope value of the straight line is B 2 Calculate C 3 All pixels in S 3 The average Euclidean distance A of all corresponding pixels in 3 , and calculate A 3 The slope value of the straight line is B 3 . Take B 1 , B 2 With B 3 The union of is the first slope value.

[0075] Exemplarily, when the colorful light source is a red light source, B 1 is (1.27, 1.33), B 2 is (1.52, 1.59), B 3 is (2.02, 2.17). Therefore, when the colorful light source is a red light source, the first slope value is set to (1.27, 1.33)∪(1.52, 1.59)∪(2.02, 2.17).

[0076] Exemplarily, when the colorful light source is a green light source, B 1 is (1.34, 1.46), B 2 is (1.71, 1.82), B 3 It is (2.30, 2.40). Therefore, when the colorful light source is a green light source, the first slope value is set to (1.34, 1.46)∪(1.71, 1.82)∪(2.30, 2.40).

[0077] Exemplarily, when the colorful light source is a blue light source, B 1 is (1.44, 1.52), B 2 is (1.79, 1.95), B 3It is (2.36, 2.43). Therefore, when the colorful light source is a blue light source, the first slope value is set to (1.44, 1.52)∪(1.79, 1.95)∪(2.36, 2.43).

[0078] Exemplarily, when the colorful light source is a yellow light source, B 1 is (1.31, 1.38), B 2 is (1.61, 1.72), B 3 is (2.16, 2.23). Therefore, when the colorful light source is a yellow light source, the first slope value is set to (1.31, 1.38)∪(1.61, 1.72)∪(2.16, 2.23).

[0079] Exemplarily, the preset number is set to [2, 4]. Preferably, the preset number is set to 2. In this way, the accuracy of the recognition result can be guaranteed. For example, if there are more than two slope values ​​among the slope values ​​corresponding to the red light source, the green light source, the blue light source, and the yellow light source that are not within the second slope value range, it is determined that the object to be identified is a real object.

[0080] Optionally, in the case of multiple colorful light sources, the second slope value is the first slope value corresponding to the multiple colorful light sources of each corresponding packaging material.

[0081] Exemplarily, when the multiple colorful light sources include a red light source, a green light source, a blue light source and a yellow light source, the second slope values ​​include: a paper second slope value, a plastic second slope value and an iron box second slope value. Among them, the second slope value of paper includes: the second slope value of red paper (1.27, 1.33), the second slope value of green paper (1.34, 1.46), the second slope value of blue paper (1.44, 1.52) and the second slope value of yellow paper (1.31, 1.38); the second slope value of plastic includes: the second slope value of red plastic (1.52, 1.59), the second slope value of green plastic (1.71, 1.82), the second slope value of blue plastic (1.79, 1.95) and the second slope value of yellow plastic (1.61, 1.72); the second slope value of iron box includes: the second slope value of red iron box (2.02, 2.17), the second slope value of green iron box (2.30, 2.40), the second slope value of blue iron box (2.36, 2.43) and the second slope value of yellow iron box (2.16, 2.23).

[0082] Since the multiple colorful light sources include red light source, green light source, blue light source and yellow light source, it is judged whether the test slope value is not within the second slope value of paper, the second slope value of plastic and the second slope value of iron box. The multiple test slope values ​​obtained include: the red test slope value corresponding to the red light source, the green test slope value corresponding to the green light source, the blue test slope value corresponding to the blue light source and the yellow test slope value corresponding to the yellow light source. When making a judgment, taking the second slope value of paper as an example, it is judged whether the red test slope value is not within the second slope value range of red paper, whether the green test slope value is not within the second slope value range of green paper, whether the blue test slope value is not within the second slope value range of blue paper, and whether the yellow test slope value is not within the second slope value range of yellow paper. If there are more than two colors whose test slope values ​​are not within the second slope value range of the corresponding color paper, it is determined that the object to be identified is a real object.

[0083] The determination process is described in detail below: For example, the multiple test slope values ​​obtained include: a red test slope value of 1.23, a green test slope value of 2.2, a blue test slope value of 2.4 and a yellow test slope value of 1.5.

[0084] First, compare with the second slope value of the paper:

[0085] Colorful light source Red light source Green light source Blue light source Yellow light source Test slope value 1.23 2.2 2.4 1.5 Second slope value (1.27，1.33) (1.34，1.46) (1.44，1.52) (1.31，1.38)

[0086] It can be seen that, when compared with the second slope value of the paper, the four test slope values ​​are not at the corresponding second slope value, and the object to be identified is not a paper.

[0087] Secondly, compare with the second slope value of plastic:

[0088] Colorful light source Red light source Green light source Blue light source Yellow light source Test slope value 1.23 2.2 2.4 1.5 Second slope value (1.52，1.59) (1.71，1.82) (1.79，1.95) (1.61，1.72)

[0089] It can be seen that when compared with the second slope value of plastic, none of the four test slope values ​​are at the corresponding second slope value, and the object to be identified is not plastic either.

[0090] Finally, compare with the second slope value of the iron box:

[0091] Colorful light source Red light source Green light source Blue light source Yellow light source Test slope value 1.23 2.2 2.4 1.5 Second slope value (2.02，2.17) (2.30，2.40) (2.36，2.43) (2.16，2.23)

[0092] It can be seen that, when compared with the second slope value of the iron box, the three test slope values ​​are not at the corresponding second slope value, and the object to be identified does not belong to the iron box. From the above example, it can be determined that the object to be identified is a real object. Optionally, the pixel comparison parameter includes a plurality of grayscale differences between the grayscale value of the test pixel point and the grayscale value of the control pixel point.

[0093] The grayscale value represents the intensity of light reflected by the object to be identified. The larger the grayscale value, the greater the intensity of light reflected by the object to be identified. The smaller the grayscale value, the smaller the intensity of light reflected by the object to be identified.

[0094] Optionally, the processor determines the authenticity of the object to be identified according to the pixel comparison parameter, including: the processor determines the average grayscale difference between the grayscale values ​​of the test pixels and the grayscale values ​​of the control pixels of the test image corresponding to the one or more colorful light sources. The processor determines the authenticity of the object to be identified according to the average grayscale difference.

[0095] Optionally, the processor determining the authenticity of the object to be identified according to the average grayscale difference includes: the processor determining that the object to be identified is a real object when the average grayscale difference satisfies a preset grayscale condition.

[0096] Wherein, when the average grayscale difference corresponds to a test image corresponding to one colorful light source, the preset grayscale condition includes that the average grayscale difference is greater than a first grayscale difference corresponding to the colorful light source. And / or, when the average grayscale difference corresponds to a test image corresponding to multiple colorful light sources, the preset grayscale condition includes that the number of average grayscale differences greater than the second grayscale difference among the multiple average grayscale differences is greater than a preset number.

[0097] It should be noted that, due to different packaging materials and different lighting colors, the first grayscale difference and the second grayscale difference are also different, and can be set according to actual conditions.

[0098] In the above embodiment, since the intensity of light reflected by a real object under a normal light source is significantly different from the intensity of light reflected by a real object under a colorful light source, the authenticity of the object to be identified can be determined based on the intensity of the reflected light. In this embodiment, the grayscale value of the test pixel is compared with the grayscale value of the control pixel, that is, the average grayscale difference between the grayscale value of the test pixel and the grayscale value of the control pixel is calculated. When the average grayscale difference is large, the difference between the intensity of light reflected under a normal light source and the intensity of light reflected by a real object under a colorful light source is large, and the object to be identified is determined to be a real object.

[0099] like Figure 4 As shown, the embodiment of the present disclosure provides another method for identifying the authenticity of an object.

[0100] The method includes:

[0101] S401: The processor obtains a detection image of the object to be identified under a normal light source.

[0102] S402: The processor determines that the object to be identified is in a moving state within a preset time period.

[0103] Optionally, the preset time period is set to [1s (second), 10s]. More specifically, the preset time period is set to 2s, 4s, 6s, 8s or 10s. By setting the preset time period to the above values, the movement state of the object to be identified can be determined.

[0104] S403: When the object to be identified is in a moving state, the processor performs target detection on the detection image and generates a corresponding recognition frame at the maximum contour of each detected target.

[0105] S404: When the area corresponding to the target detection has at least a first identification frame and a second identification frame, and the first identification frame is within the second identification frame, the processor marks the framed area of ​​the to-be-identified object corresponding to the first identification frame as a to-be-confirmed area.

[0106] S405, the processor obtains an image to be identified in which the area to be confirmed is under a colorful light source, and determines the authenticity of the object to be identified based on the image to be identified.

[0107] In the above embodiment, it is disclosed that the authenticity of the object to be identified is confirmed by the above method when the object to be identified is in a moving state. In this way, the identification environment and usage scenarios are expanded, so that the authenticity of the object to be identified can be accurately identified whether the object to be identified is in a dynamic or static state.

[0108] Optionally, the processor determines that the object to be identified is in a moving state within a preset period of time in the following manner: the processor sequentially obtains pixel change rates of the object to be identified in a preset area within the preset period of time. The processor determines that the object to be identified is in a moving state within the preset period of time when the maximum pixel change rate among the pixel change rates is greater than or equal to the preset change rate.

[0109] The preset area is an area in the storage device where the items to be identified are placed.

[0110] Exemplarily, the preset change rate is set to 0. When the maximum pixel change rate among the pixel change rates is greater than the preset change rate, the processor indicates that the pixels of the image of the object to be identified have changed within the preset period, and determines that the object to be identified is in a moving state.

[0111] Combination Figure 5, the embodiment of the present disclosure provides a device 200 for identifying the authenticity of an item, including a processor 500 and a memory 501. Optionally, the device 200 for identifying the authenticity of an item may also include a communication interface 502 and a bus 503. The processor 500, the communication interface 502, and the memory 501 may communicate with each other through the bus 503. The communication interface 502 may be used for information transmission. The processor 500 may call the logic instructions in the memory 501 to execute the method for identifying the authenticity of an item in the above embodiment.

[0112] In addition, the logic instructions in the memory 501 described above can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0113] The memory 501 is a storage medium that can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 500 executes the function application and data processing by running the program instructions / modules stored in the memory 501, that is, the method for identifying the authenticity of an item in the above embodiment is implemented.

[0114] The memory 501 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 501 may include a high-speed random access memory and may also include a non-volatile memory.

[0115] Combination Figure 6 As shown, an embodiment of the present disclosure provides a storage device 1. The storage device 1 includes: a storage device body 10, a light source device, a shooting device, and a device 200 for identifying the authenticity of an item as described above. The installation relationship described here is not limited to placement inside the storage device 1, but also includes installation connections with other components of the storage device 1, including but not limited to physical connections, electrical connections, or signal transmission connections. It can be understood by those skilled in the art that the device 200 for identifying the authenticity of an item can be adapted to a feasible storage device body, thereby realizing other feasible embodiments.

[0116] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned method for identifying the authenticity of an object.

[0117] The computer-readable storage medium mentioned above may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0118] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes, or a transient storage medium.

[0119] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0120] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0121] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0122] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for identifying the authenticity of an object, characterized in that: include: Obtain a detection image of the object to be identified under normal light source; Performing target detection on the detection image, generating a corresponding identification frame at the maximum contour of each detection target; wherein the detection target has the appearance contour characteristics of the object to be identified; When the area corresponding to the target detection has at least a first identification frame and a second identification frame, and the first identification frame is within the second identification frame, marking the framed area of ​​the to-be-identified object corresponding to the first identification frame as a to-be-confirmed area; An image to be identified is obtained in which the area to be identified is under a colorful light source, and the authenticity of the object to be identified is determined based on the image to be identified.

2. The method according to claim 1, characterized in that: Obtaining an image to be identified in which the area to be identified is under a colorful light source, including: The image of the area to be confirmed under normal light source is used as a control image; Using an image of the area to be confirmed under one or more colorful light sources as a test image; Among them, the proportion of specific color attributes of colorful light sources is greater than the proportion of specific color attributes of normal light sources.

3. The method according to claim 2, characterized in that Determine the authenticity of the object to be identified based on the image to be identified, including: Determining pixel comparison parameters of a test pixel of the test image relative to a control pixel of the control image; wherein the pixel comparison parameters include a plurality of Euclidean distances between the test pixel and the control pixel; The authenticity of the object to be identified is determined based on the pixel comparison parameters.

4. The method according to claim 3, characterized in that Determine the authenticity of the object to be identified based on pixel comparison parameters, including: Determine the average Euclidean distance between the test pixel points and the control pixel points of the test image corresponding to the one or more colorful light sources; Map the straight line corresponding to the average Euclidean distance to a two-dimensional coordinate system; The trial slope value of the straight line corresponding to the average Euclidean distance is determined, and the authenticity of the object to be identified is determined based on the trial slope value.

5. The method according to claim 4, characterized in that The average Euclidean distance is calculated as follows: Determine a plurality of Euclidean distances between a plurality of test pixels of a test image corresponding to each colorful light source and a plurality of corresponding control pixels; The multiple Euclidean distances of the test images corresponding to each colorful light source are averaged to obtain the average Euclidean distance of each colorful light source.

6. The method according to claim 4, characterized in that The authenticity of the object to be identified is determined based on the test slope value, including: When the test slope value satisfies the preset slope condition, it is determined that the object to be identified is a real object; Wherein, when the test slope value corresponds to a test image corresponding to a colorful light source, the preset slope condition includes that the test slope value is not within a first slope value range corresponding to the colorful light source; and / or, In the case where the test slope value corresponds to a test image corresponding to a plurality of colorful light sources, the preset slope condition includes that the number of test slope values ​​that are not within the second slope value range among the plurality of test slope values ​​is greater than a preset number.

7. The method according to any one of claims 1 to 6, characterized in that: Also includes: Before performing target detection on the detection image, it is determined that the object to be identified is in a moving state within a preset time period.

8. The method according to claim 7, characterized in that Determine whether the object to be identified is in a moving state within a preset time period in the following manner: In a preset period of time, sequentially obtain the pixel change rate of the object to be identified in the preset area; If the maximum pixel change rate among the pixel change rates is greater than the preset change rate, it is determined that the object to be identified is in a moving state within the preset time period.

9. A device for identifying the authenticity of an article, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the method for identifying the authenticity of an object as claimed in any one of claims 1 to 8 when running the program instructions.

10. A storage device, characterized in that: include: Storage equipment body; The light source device is disposed on the storage device body and is configured to provide a normal light source and a colorful light source; A photographing device, disposed on the storage device body, configured to photograph the image of the object to be identified under normal light source and colorful light source; and, The device for identifying the authenticity of an article as claimed in claim 9 is installed on a storage device body.

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