A multifunctional integrated management method and system for garbage transfer and classification

Abstract

The present application relates to the field of image enhancement learning technology, and specifically to a multifunctional garbage transfer and classification integrated management method and system, the method comprising: analyzing the similarity of color components between adjacent pixels in each image block, and evaluating the similarity of the color component probability distribution between each image block and the garbage image in its color channel, i.e., the similarity coefficient; analyzing the difference between the similarity coefficients of all reference blocks in the suspected occlusion direction of each image block and the other directions, and analyzing the distance from the triplets of all reference blocks in the suspected occlusion direction to the fitting line, and determining the color loss value based on the color difference; and identifying and classifying garbage based on the similarity coefficient and color difference. The present application optimizes and adjusts the preprocessing stage of the intelligent garbage image recognition process to improve the accuracy of garbage recognition and classification, as well as the efficiency of the integrated management of garbage transfer and classification.

Description

Technical Field

[0001] The present application relates to the field of image enhancement learning technology, and in particular to a multifunctional integrated management method and system for waste transfer and classification. Background Art

[0002] As an effective measure, waste sorting is of great significance to ecological and environmental protection and sustainable economic development. Multifunctional waste transfer and sorting integration aims to integrate multiple waste treatment functions into a single waste recycling facility or system. This model enables efficient sorting, comprehensive transfer, and one-stop disposal of domestic waste, significantly improving the overall efficiency and convenience of waste disposal.

[0003] Currently, waste sorting largely relies on residents' own initiative. However, this approach presents numerous challenges, including low efficiency, high cost, and a high risk of misclassification. Therefore, the search for efficient and intelligent waste sorting solutions is of great practical significance and urgency. To address these challenges, some regions have introduced smart waste sorting rooms with sorting capabilities for secondary waste sorting. These smart waste rooms integrate a range of intelligent features, including automatic bag opening, AI-powered automatic recognition based on deep learning algorithms, automatic sorting, automatic bucket changing, and automatic conveying. However, in actual operation, it has been found that the accuracy of AI-powered automatic recognition plays a crucial role in waste sorting efficiency. Due to the variable lighting conditions in the working environment of smart waste rooms and the potential for obstruction caused by internal mechanical equipment, these interfering factors inevitably introduce noise, resulting in uneven illumination intensity in the images being identified. This condition directly impacts the accuracy of the deep learning algorithm in identifying and sorting the sorted waste, reducing both the accuracy of waste identification and classification, as well as the efficiency of integrated waste transfer and sorting management. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a multifunctional integrated management method and system for waste transfer and classification. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a multifunctional integrated management method for waste transfer and classification, the method comprising the following steps:

[0006] S1: Get the garbage image on each RGB color channel, and record the component of the pixel value of each pixel in each color channel as the color component of each pixel;

[0007] S2: Enhance the garbage image based on the distribution characteristics of the pixel color components in each color channel. The specific process is as follows:

[0008] S201: Dividing the junk image of each color channel into multiple image blocks, analyzing the similarity of color components between adjacent pixels in each image block in each color channel, determining a first similarity for each image block in each color channel, evaluating the similarity of color component probability distribution between each image block and the junk image in the color channel in which it resides, determining a second similarity for each image block in each color channel, and combining the first similarity to determine a similarity coefficient for each image block in each color channel;

[0009] S202: In the garbage image of each color channel, extending in different directions from each image block as a starting point, using a preset number of image blocks in each direction of each image block as reference blocks in each direction of each image block, combining similarity coefficients of all reference blocks in each direction of each image block, and screening suspected occlusion directions from all directions; analyzing the difference between the similarity coefficients of the suspected occlusion direction of each image block and all reference blocks in other directions, and determining the color difference of each image block in each color channel;

[0010] S203: The coordinates and similarity coefficients of each image block are combined into a triplet, and the triplet of each image block and all reference blocks in the suspected occlusion direction is fitted to obtain a fitted line. The distance from the triplets of all reference blocks in the suspected occlusion direction to the fitted line is analyzed, and the color loss value of each image block in each color channel is determined based on the color difference.

[0011] S204: Analyzing the difference in color loss value between each image block and all other image blocks in the garbage image of each color channel, and performing enhancement processing on the garbage image in combination with the similarity coefficient;

[0012] S3: Identify and classify the enhanced garbage images.

[0013] Preferably, the method for determining the first similarity of each image block in each color channel is:

[0014] In the garbage image of each color channel, the gray level co-occurrence matrix of each image block is obtained, and the inverse difference moment of the gray level co-occurrence matrix of each image block is used as the first similarity of each image block under each color channel.

[0015] Preferably, the method for determining the second similarity of each image block in each color channel is:

[0016] Perform probability distribution statistics on the color components of all pixels in each image block in the garbage image under each color channel, and the color components of all pixels in the garbage image under each color channel, and fit the results of the probability distribution statistics to obtain the probability distribution curve of each image block under each color channel, and the probability distribution curve of the garbage image under each color channel;

[0017] The similarity of the probability distribution curve between each image block in each color channel and the corresponding garbage image is used as the second similarity of each image block in each color channel.

[0018] Preferably, the similarity coefficient of each image block in each color channel is the product of the first similarity and the second similarity of each image block in each color channel.

[0019] Preferably, the method for screening out suspected occlusion directions from all directions is:

[0020] In the garbage image of each color channel, the cumulative sum of the similarity coefficients of all reference blocks in each direction of each image block is calculated, and the direction corresponding to the maximum cumulative sum is used as the suspected occlusion direction of each image block.

[0021] Preferably, the method for determining the color difference of each image block is:

[0022] The difference between the similarity coefficients of the suspected occlusion direction of each image block and all reference blocks in the other directions is recorded as the color loss difference between the suspected occlusion direction of each image block and the other directions;

[0023] The average of the color loss differences between the suspected occlusion direction of each image block and all other directions is taken as the color difference of each image block.

[0024] Preferably, the method for determining the color loss value of each image block is:

[0025] In the garbage image of each color channel, calculate the mean distance from the triplets of all reference blocks to the fitting straight line in the suspected occlusion direction of each image block, and record it as the mean distance of each image block;

[0026] The color loss value of each image patch is the inverse of the product of the color difference of each image patch and the mean distance.

[0027] Preferably, the enhancing of the junk image includes:

[0028] In the garbage image of each color channel, the sum of the differences in color loss values ​​between each image block and all other image blocks is recorded as the detail loss difference of each image block;

[0029] The product of the detail loss difference of each image block and the similarity coefficient is used as the loss factor of each image block;

[0030] The optimized CLF parameter R of image block i under color channel u u,i The expression is: R u,i =S+γ×T u,i; Wherein, S and γ represent the preset first value and the preset second value respectively; T u,i Represents the normalized value of the loss factor of image block i under color channel u;

[0031] Each image block in the garbage image under each color channel is used as the input of the limited contrast adaptive histogram equalization algorithm, wherein the optimized CLF parameter of each image block is used as the CLF parameter in the limited contrast adaptive histogram equalization algorithm, and each enhanced image block is output. All image blocks in the garbage images of all color channels are traversed to obtain the enhanced garbage images of all color channels, and the garbage images of all color channels are fused to obtain the enhanced garbage images.

[0032] Preferably, the identifying and classifying the enhanced junk images includes:

[0033] Obtain a large amount of garbage sample data and its category labels, train the deep learning model, use the enhanced garbage images as the input of the trained deep learning model, output the category labels of the enhanced garbage images, and transfer the garbage to the corresponding storage area according to the category labels of its garbage images.

[0034] In the second aspect, an embodiment of the present application also provides a multifunctional garbage transfer and classification integrated management system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned multifunctional garbage transfer and classification integrated management methods.

[0035] This application has at least the following beneficial effects:

[0036] This application constructs a similarity coefficient by analyzing the similarity of texture features between an image block and a garbage image in its color channel, which can help identify areas of detail loss in garbage images that may be caused by occlusion, thereby providing a basis for subsequent image enhancement processing, and thus improving the efficiency of garbage identification and classification; further, by analyzing the differences in color intensity distribution characteristics of image blocks along different directions, a color loss value is constructed, which can further assist in judging the possibility and degree of occlusion of image blocks, thereby performing image enhancement processing on areas of detail loss caused by occlusion, improving the efficiency of image enhancement while further improving the efficiency of garbage identification and classification; further, the image enhancement algorithm is optimized by combining the color loss value and the similarity coefficient, and the image details of the garbage occluded area are amplified. Based on the enhanced garbage image, the garbage is classified and processed in combination with a deep learning model, thereby improving the efficiency of garbage identification and classification. This application significantly improves the accuracy of garbage identification and classification and the efficiency of integrated management of garbage transfer and classification by optimizing the preprocessing of garbage images and reducing the impact of uneven lighting and occlusion on garbage identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0038] Figure 1 A flowchart of the steps of a multifunctional integrated management method for waste transfer and classification provided in one embodiment of the present application;

[0039] Figure 2 A schematic diagram of a color difference extraction process provided in one embodiment of the present application;

[0040] Figure 3 A flowchart of the steps for enhancing junk images provided in one embodiment of the present application. DETAILED DESCRIPTION

[0041] To further illustrate the technical means and effectiveness of this application to achieve the intended invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of a multifunctional integrated management method and system for waste transfer and classification proposed in this application. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0042] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0043] The following describes in detail a multifunctional integrated management method and system for waste transfer and classification provided by this application with reference to the accompanying drawings.

[0044] See also Figure 1 , which shows a flowchart of a multifunctional integrated management method for waste transfer and classification provided by an embodiment of the present application, the method comprising the following steps:

[0045] S1: Obtain the garbage image on each RGB color channel, and record the component of the pixel value of each pixel in each color channel as the color component of each pixel.

[0046] The intelligent garbage room used in this embodiment implements multifunctional integrated management of transfer and classification. Its management system covers several key modules: garbage collection, intelligent identification, classified transmission, and transfer storage. These modules work closely together to ensure the orderly operation of garbage disposal. Among them, the garbage classification module is mainly responsible for providing residents with a convenient and hygienic garbage delivery channel, realizing contactless garbage delivery, minimizing the risk of cross-infection, and smoothly transporting the garbage delivered by residents to subsequent processing links. The intelligent identification module is the core task of accurately classifying and identifying garbage. It uses the powerful capabilities of deep learning models to accurately determine the category of garbage, providing a key basis for subsequent classified transmission and is an important step in achieving fine garbage classification. The classified transmission module, based on the garbage category information output by the intelligent identification module, accurately transfers different categories of garbage to the corresponding garbage bins or storage areas, achieving efficient classified collection and laying the foundation for subsequent garbage transfer storage and final processing. The transfer storage module temporarily stores the classified garbage, acting as a buffer and transfer, so that it can be uniformly transferred and processed according to the corresponding garbage disposal process, ensuring the continuity and stability of the entire garbage disposal system.

[0047] Considering that the brightness of garbage images may be uneven due to environmental changes during the garbage classification and identification process, which may affect the accuracy of classification, this embodiment obtains garbage images after bag breaking from the classification table in the intelligent garbage room.

[0048] Since the brightness of garbage images is unstable during classification in the intelligent garbage room, and some areas may have low brightness due to ambient light or occlusion, in the actual processing process, this application optimizes and adjusts the image based on the brightness distribution characteristics of the garbage image before classification to improve the accuracy of intelligent garbage recognition; during the image acquisition process in the intelligent garbage room, the acquired garbage image is a color image in the RGB color space. In order to completely retain the color information of the image, this embodiment extracts the images of the garbage image above and below each RGB color channel, and records the component of the pixel value of each pixel in each color channel as the color component of each pixel, and conducts independent analysis on the color intensity change characteristics of the garbage image in each color channel.

[0049] S2: Enhance the garbage image according to the distribution characteristics of the color components of the pixels in the garbage image under each color channel.

[0050] S201: Divide the junk image of each color channel into multiple image blocks, analyze the similarity of color components between adjacent pixels in each image block, determine a first similarity for each image block in each color channel, evaluate the similarity of the color component probability distribution between each image block and the junk image of the color channel in which it resides, determine a second similarity for each image block in each color channel, and determine a similarity coefficient for each image block in each color channel based on the first similarity.

[0051] Due to the complex and changing lighting conditions within smart garbage rooms, interference factors such as garbage stacking and mechanical equipment can cause uneven brightness in captured garbage images. This can alter the contrast between different areas of the garbage image, causing detail loss and difficulty in identifying features, hindering the deep learning model's ability to extract image features. The more severe the uneven brightness, the lower the accuracy of intelligent recognition, which in turn reduces garbage sorting efficiency.

[0052] When garbage images are placed in an environment with good lighting conditions, different garbage items show richer colors. This is reflected in the garbage images of each color channel, where the distribution of color components is more complex, and the edge positions of garbage targets are more prominent in the image. Correspondingly, the image contains richer details. Conversely, in areas covered by garbage or with low light intensity, the garbage image contains relatively less detailed information. Therefore, by analyzing the changes in the color components of pixels in local areas of the garbage image, we can determine whether there is occlusion in the garbage image and perform image enhancement processing on the occluded areas to further improve the efficiency of garbage classification. Specifically:

[0053] (1) Given the differences in details contained in different regions of the garbage image, in order to more accurately obtain the features of each local region, this embodiment divides the garbage image of each color channel into multiple continuous and non-overlapping image blocks. It should be noted that the number of divided image blocks is inversely proportional to the size of the image area covered by each image block, that is, the more divided image blocks there are, the smaller the image area covered by each image block, and conversely, the fewer divided image blocks there are, the larger the image area covered by each image block.

[0054] (2) Furthermore, in the garbage image of each color channel, the gray level co-occurrence matrix of each image block is obtained, and the inverse difference moment of the gray level co-occurrence matrix of each image block is used as the first similarity of each image block under each color channel; the first similarity reflects the uniformity of the pixel value distribution difference of different image blocks in the garbage image. The larger the first similarity, the more uniform the pixel distribution in the local area of ​​the garbage image, that is, the local detail features in the garbage image will be likely to be weakened, indicating that the possibility of the garbage image being blocked is greater.

[0055] Among them, the method for obtaining the gray level co-occurrence matrix and the method for calculating the inverse difference moment are both well-known technologies, and the process of obtaining the gray level co-occurrence matrix and the process of calculating the inverse difference moment are not repeated here.

[0056] (3) Furthermore, considering the local difference in the influence of occlusion on the detail features in the local area after the garbage bag is broken, the second similarity of each image block is constructed by analyzing the similarity between the image block and the garbage image in each color channel to determine the degree of occlusion of the garbage, specifically:

[0057] Perform probability distribution statistics on the color components of all pixels in each image block in the garbage image under each color channel, and the color components of all pixels in the garbage image under each color channel, and fit the results of the probability distribution statistics to obtain the probability distribution curve of each image block under each color channel, and the probability distribution curve of the garbage image under each color channel;

[0058] It should be noted that in this embodiment, the probability distribution histogram of the color components of all pixels in the garbage image under each color channel and the probability distribution histogram of the color components of all pixels in each image block are statistically analyzed, and the probability distribution histograms are fitted to obtain the probability distribution curve. Among them, the methods for obtaining the histograms and the probability distribution curves are both well-known technologies, and the specific process will not be repeated here.

[0059] Furthermore, it should be understood that there are many commonly used fitting methods. In this embodiment, the least squares method is used to fit the statistical results of the probability distribution. The implementer may also use other fitting methods such as polynomial fitting. This embodiment does not impose any particular limitation on the choice of fitting method. The least squares method is a well-known technique, and its specific fitting process will not be described in detail.

[0060] (4) Furthermore, the similarity of the probability distribution curve between each image block in each color channel and the corresponding garbage image is used as the second similarity of each image block in each color channel; the greater the second similarity, the more serious the loss of detail features in the garbage image due to environmental occlusion and interference during the garbage processing process.

[0061] It should be noted that there are many methods for measuring the similarity between curves. In this embodiment, the KL divergence of the probability distribution curve between each image block and the corresponding spam image in each color channel is used as the similarity between the probability distribution curves between each image block and the corresponding spam image in each color channel. In actual applications, as other implementations, implementers may also use other methods for measuring the similarity between curves, such as cosine similarity. This embodiment does not impose any specific restrictions on the method used to measure the similarity between curves. The calculation method of the KL divergence is well known, and its specific calculation process is not repeated here.

[0062] (5) Further combine the first similarity and the second similarity to determine the similarity coefficient and determine the degree of obstruction and interference of the garbage, specifically:

[0063] The product of the first similarity and the second similarity of each image block in each color channel is used as the similarity coefficient of each image block in each color channel.

[0064] According to the similarity of each image block under each color channel, it can be understood that if the first similarity of the current image block is greater and the second similarity is greater, the similarity coefficient of the current image block is greater, indicating that the image of the corresponding area of ​​the image block in the garbage image has caused serious detail loss due to environmental occlusion interference. Therefore, the image of the corresponding area in the garbage image where the image block is located is enhanced, so that the garbage can be more accurately identified and classified based on the enhanced texture details, thereby improving the garbage classification efficiency; conversely, if the first similarity of the current image block is smaller and the second similarity is smaller, the similarity coefficient of the current image block is greater, indicating that the image of the corresponding area of ​​the image block in the garbage image is less affected by environmental occlusion interference, which can speed up the image enhancement processing progress, thereby improving the garbage classification efficiency.

[0065] S202: In the garbage image of each color channel, extend in different directions with each image block as the starting point, use a preset number of image blocks in each direction of each image block as reference blocks in each direction of each image block, combine the similarity coefficients of all reference blocks in each direction of each image block, and screen out suspected occlusion directions from all directions; analyze the difference between the similarity coefficients of each image block in the suspected occlusion direction and all reference blocks in other directions to determine the color difference of each image block.

[0066] Considering the randomness of garbage bag breakage during the garbage disposal process, the detail loss characteristics due to occlusion in garbage disposal images collected at different times have extension characteristics in different directions. That is, due to different occlusion angles, the detail loss in different directions caused by light occlusion decreases as the light extends in the direction. Therefore, based on the above analysis, this embodiment performs extension comparison in different directions for each image block. Specifically:

[0067] In the garbage images of each color channel, each image block is used as a starting point, and extended comparisons are performed in the directions of 0 degrees, 45 degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, 270 degrees, and 315 degrees, respectively. A preset number of image blocks in each direction of each image block are used as reference blocks in each direction of each image block.

[0068] It should be noted that the value of the preset number is set manually. In this embodiment, the value of the preset number is 5. The implementer can also set it by himself according to the specific situation. This embodiment does not impose any special restrictions.

[0069] Furthermore, if the similarity coefficient between a reference block extending in a certain angular direction and the image block is relatively close, and the similarity coefficient gradually decreases in that direction, while the similarity coefficient difference between the reference block extending in other directions and the image block at the corresponding starting point is relatively large and does not show a gradually decreasing trend, then it means that the occlusion extension feature of the image block is more obvious. Therefore, the similarity coefficients of all reference blocks in each direction of each image block are combined to screen out suspected occlusion directions from all directions; the difference between the similarity coefficients of each image block in the suspected occlusion direction and all reference blocks in the remaining directions is analyzed to determine the color difference of each image block, specifically:

[0070] In the garbage image of each color channel, the cumulative sum of the similarity coefficients of all reference blocks in each direction of each image block is calculated, and the direction corresponding to the maximum cumulative sum is used as the suspected occlusion direction of each image block.

[0071] The difference between the similarity coefficients of all reference blocks in the suspected occlusion direction of each image block and the other directions is recorded as the color loss difference between the suspected occlusion direction and the other directions of each image block; if the color loss difference of the current image block is larger, it means that the color difference between the suspected occlusion direction and other directions is more obvious, which means that the current image block is more likely to be interfered by environmental occlusion, and it is more necessary to enhance the area corresponding to the image block in the garbage image; conversely, if the color loss difference of the current image block is smaller, it means that the current image block is less likely to be interfered by environmental occlusion.

[0072] It should be noted that there are many methods for measuring the differences between data groups. In this embodiment, the DTW distance between the similarity coefficients of each image block in the suspected occlusion direction and all reference blocks in the other directions is used as the difference between the similarity coefficients of each image block in the suspected occlusion direction and all reference blocks in the other directions. In actual application, as other implementation methods, the implementer may also adopt other methods for measuring the differences between data groups, such as Euclidean distance or Manhattan distance. This embodiment does not impose any special restrictions on the selection of methods for measuring the differences between data groups.

[0073] The calculation method of the DTW distance is a well-known technology, and its specific calculation process is not repeated here.

[0074] Furthermore, the average of the color loss differences between the suspected occlusion direction and all other directions of each image block is taken as the color difference of each image block; if the color difference of the current image block is smaller, it means that the influence of detail loss of the current image block along the suspected occlusion direction is gradually weakened, indicating that the current image block is more likely to be occluded by the environment; conversely, if the color difference of the current image block is larger, it means that the distribution difference of the color components of the pixels of the current image block along the suspected occlusion direction is larger, and the current image block is less likely to be occluded by the environment.

[0075] Preferably, the color difference extraction process diagram provided in this embodiment is as follows Figure 2 shown.

[0076] S203: The coordinates and similarity coefficients of each image block are combined into a triplet, and the triplet of each image block and all reference blocks in the suspected occlusion direction is fitted to obtain a fitted straight line. The distance from the triplets of all reference blocks in the suspected occlusion direction to the fitted straight line is analyzed, and the color loss value of each image block is determined in combination with the color difference.

[0077] In garbage images, the occluded portion of the garbage usually occupies a small portion of the entire garbage image. The more severe the uneven illumination caused by occlusion, the smaller the color difference of the detail loss extension feature between each image block. Therefore, by analyzing the change trend of the color component of each image block along the suspected occlusion direction, the color loss value of each image block is constructed, specifically:

[0078] The coordinates and similarity coefficients of each image block are combined into a triplet, and the triplet of each image block and all reference blocks in the suspected occlusion direction is fitted to obtain a fitted line. There are many commonly used fitting methods. In this embodiment, multiple linear regression is used to fit the triplet to obtain a fitted line. In actual applications, as other implementations, implementers may also use other fitting methods such as multivariate polynomial regression. This embodiment does not impose any special restrictions on the choice of fitting method. Multiple linear regression is a well-known technology, and its specific principles and processes are not detailed here.

[0079] Furthermore, in the garbage image of each color channel, the mean distance between the triplets of all reference blocks and the fitting straight line in the suspected occlusion direction of each image block is calculated, and recorded as the mean distance of each image block;

[0080] The color loss value of each image patch is the inverse of the product of the color difference of each image patch and the mean distance.

[0081] According to the color loss value of each image block, it can be understood that if the color difference of the current image block is smaller and the mean distance of the image block is smaller, the color loss value of the current image block is larger, indicating that the color difference between the image block and the reference block in the suspected occlusion direction is smaller, indicating that the possibility of the current image block being occluded is greater; conversely, if the color difference of the current image block is larger and the mean distance of the image block is larger, the color loss value of the current image block is smaller, indicating that the color difference between the image block and the reference block in the suspected occlusion direction is larger, indicating that the possibility of the current image block being occluded is smaller.

[0082] S204: In the garbage image of each color channel, analyze the difference in color loss value between each image block and all other image blocks, and perform enhancement processing on the garbage image in combination with the similarity coefficient.

[0083] To address the issue of uneven brightness in garbage images due to environmental changes, the color loss value of each image block is obtained from the above analysis. This value reflects the degree of occlusion of each image block. Therefore, image enhancement processing is performed on the corresponding area of ​​the image block in the garbage image based on the degree of occlusion of the image block. Specifically,

[0084] In the garbage image of each color channel, the sum of the differences in color loss values ​​between each image block and all other image blocks is recorded as the detail loss difference of each image block;

[0085] Furthermore, the product of the detail loss difference of each image block and the similarity coefficient is used as the loss factor of each image block;

[0086] According to the loss factor of each image block, it can be understood that if the difference in detail loss of the image block is greater and the similarity coefficient is greater, the loss factor is greater, indicating that the detail loss of the image block is more serious and the possibility of the image block being occluded is greater, and the image block should be enhanced; conversely, if the difference in detail loss of the image block is smaller and the similarity coefficient is smaller, the loss factor is smaller, indicating that the possibility of the image block being occluded is smaller.

[0087] Furthermore, based on the loss factor of each image block, the contrast-limited adaptive histogram equalization algorithm is improved. Specifically:

[0088] The optimized CLF parameter R of image block i under color channel u u,i The expression is: R u,i =S+γ×T u,i ; Wherein, S and γ represent the preset first value and the preset second value respectively; T u,i Represents the normalized value of the loss factor of image patch i under color channel u.

[0089] It should be noted that, since the value range of the CLF parameter in the contrast-limited adaptive histogram equalization algorithm is generally between [2, 5], the preset first value S and the preset second value γ in this embodiment are 2 and 3 respectively. There is no limit on the size between the preset first value and the preset second value, and the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0090] Furthermore, each image block in the garbage image under each color channel is used as the input of the contrast-constrained adaptive histogram equalization algorithm, wherein the optimized CLF parameter of each image block is used as the CLF parameter in the contrast-constrained adaptive histogram equalization algorithm, and each enhanced image block is output. All image blocks in the garbage images of all color channels are traversed to obtain enhanced garbage images of all color channels, and the garbage images of all color channels are fused to obtain the enhanced garbage images.

[0091] The process of fusing the garbage images of all color channels is a well-known technology, and its specific principle will not be described in detail.

[0092] Preferably, the flowchart of the garbage image enhancement steps provided in this embodiment is as follows: Figure 3 shown.

[0093] S3: Identify and classify the enhanced garbage images.

[0094] In this embodiment, a large amount of garbage sample data is obtained and the garbage sample data is labeled by category. All garbage sample data and their category standards are used as a convolutional neural network to train a trained convolutional neural network, and the enhanced garbage image is used as the trained convolutional neural network to output the category label of the enhanced garbage image, and the garbage is transferred to the corresponding storage area according to the category label of its garbage image.

[0095] Among them, convolutional neural network is a well-known technology, and its specific principles are not described in detail.

[0096] So far, this embodiment addresses the problem of low intelligent recognition accuracy in the actual processing process caused by uneven image illumination due to variable light and occlusion in the intelligent recognition module. By integrating the detail-rich features and color distribution characteristics of each image block area in the garbage image, the preprocessing stage of the image intelligent recognition process is optimized and adjusted, thereby reducing the impact of uneven illumination on the accuracy of garbage recognition and classification, and effectively improving the integrated management efficiency of garbage transfer and classification.

[0097] Based on the same inventive concept as the above method, an embodiment of the present application also provides a multifunctional garbage transfer and classification integrated management system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned multifunctional garbage transfer and classification integrated management methods.

[0098] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0099] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0100] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A multifunctional integrated management method for waste transfer and classification, characterized in that: The method comprises the following steps: S1: Get the garbage image on each RGB color channel, and record the component of the pixel value of each pixel in each color channel as the color component of each pixel; S2: Enhance the garbage image based on the distribution characteristics of the pixel color components in each color channel. The specific process is as follows: S201: Dividing the junk image of each color channel into multiple image blocks, analyzing the similarity of color components between adjacent pixels in each image block in each color channel, determining a first similarity for each image block in each color channel, evaluating the similarity of color component probability distribution between each image block and the junk image in the color channel in which it resides, determining a second similarity for each image block in each color channel, and combining the first similarity to determine a similarity coefficient for each image block in each color channel; S202: In the garbage image of each color channel, extending in different directions from each image block as a starting point, using a preset number of image blocks in each direction of each image block as reference blocks in each direction of each image block, combining similarity coefficients of all reference blocks in each direction of each image block, and screening suspected occlusion directions from all directions; analyzing the difference between the similarity coefficients of the suspected occlusion direction of each image block and all reference blocks in other directions, and determining the color difference of each image block in each color channel; S203: The coordinates and similarity coefficients of each image block are combined into a triplet, and the triplet of each image block and all reference blocks in the suspected occlusion direction is fitted to obtain a fitted line. The distance from the triplets of all reference blocks in the suspected occlusion direction to the fitted line is analyzed, and the color loss value of each image block in each color channel is determined based on the color difference. S204: Analyzing the difference in color loss value between each image block and all other image blocks in the garbage image of each color channel, and performing enhancement processing on the garbage image in combination with the similarity coefficient; S3: Identify and classify the enhanced garbage images.

2. A multifunctional integrated management method for waste transfer and classification according to claim 1, characterized in that: The method for determining the first similarity of each image block in each color channel is: In the garbage image of each color channel, the gray level co-occurrence matrix of each image block is obtained, and the inverse difference moment of the gray level co-occurrence matrix of each image block is used as the first similarity of each image block under each color channel.

3. A multifunctional integrated management method for waste transfer and classification according to claim 1, characterized in that: The method for determining the second similarity of each image block in each color channel is: Perform probability distribution statistics on the color components of all pixels in each image block in the garbage image under each color channel, and the color components of all pixels in the garbage image under each color channel, and fit the results of the probability distribution statistics to obtain the probability distribution curve of each image block under each color channel, and the probability distribution curve of the garbage image under each color channel; The similarity of the probability distribution curve between each image block in each color channel and the corresponding garbage image is used as the second similarity of each image block in each color channel.

4. A multifunctional integrated management method for waste transfer and classification according to claim 1, characterized in that: The similarity coefficient of each image block under each color channel is the product of the first similarity and the second similarity of each image block under each color channel.

5. A multifunctional integrated management method for waste transfer and classification according to claim 1, characterized in that: The method for filtering out suspected occlusion directions from all directions is: In the garbage image of each color channel, the cumulative sum of the similarity coefficients of all reference blocks in each direction of each image block is calculated, and the direction corresponding to the maximum cumulative sum is used as the suspected occlusion direction of each image block.

6. A multifunctional integrated management method for waste transfer and classification according to claim 1, characterized in that: The method for determining the color difference of each image block is: The difference between the similarity coefficients of the suspected occlusion direction of each image block and all reference blocks in the other directions is recorded as the color loss difference between the suspected occlusion direction of each image block and the other directions; The average of the color loss differences between the suspected occlusion direction of each image block and all other directions is taken as the color difference of each image block.

7. A multifunctional integrated management method for waste transfer and classification according to claim 1, characterized in that: The method for determining the color loss value of each image block is: In the garbage image of each color channel, calculate the mean distance from the triplets of all reference blocks to the fitting straight line in the suspected occlusion direction of each image block, and record it as the mean distance of each image block; The color loss value of each image patch is the inverse of the product of the color difference of each image patch and the mean distance.

8. A multifunctional integrated management method for waste transfer and classification according to claim 1, characterized in that: The enhancing of the junk image comprises: In the garbage image of each color channel, the sum of the differences in color loss values ​​between each image block and all other image blocks is recorded as the detail loss difference of each image block; The product of the detail loss difference of each image block and the similarity coefficient is used as the loss factor of each image block; The optimized CLF parameter R of image block i under color channel u u,i The expression is: R u,i =S+γ×T u,i ; Wherein, S and γ represent the preset first value and the preset second value respectively; T u,i Represents the normalized value of the loss factor of image block i under color channel u; Each image block in the garbage image under each color channel is used as the input of the limited contrast adaptive histogram equalization algorithm, wherein the optimized CLF parameter of each image block is used as the CLF parameter in the limited contrast adaptive histogram equalization algorithm, and each enhanced image block is output. All image blocks in the garbage images of all color channels are traversed to obtain the enhanced garbage images of all color channels, and the garbage images of all color channels are fused to obtain the enhanced garbage images.

9. A multifunctional integrated management method for waste transfer and classification according to claim 1, characterized in that: The identifying and classifying process of the enhanced garbage images includes: Obtain a large amount of garbage sample data and its category labels, train the deep learning model, use the enhanced garbage images as the input of the trained deep learning model, output the category labels of the enhanced garbage images, and transfer the garbage to the corresponding storage area according to the category labels of its garbage images.

10. A multifunctional integrated management system for waste transfer and classification, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a multifunctional garbage transfer and classification integrated management method as described in any one of claims 1 to 9 are implemented.

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