Clothes identification and analysis method of washing and care robot based on image processing technology

Abstract

The invention belongs to the technical field of clothes identification, and discloses a washing robot clothes identification analysis method based on an image processing technology. According to the invention, when the clothes are identified, the fading possibility of the specified clothes is analyzed based on the acquired image, and then the specified clothes are classified, so that the actual condition of the clothes can be intuitively and accurately known through the analysis mode, and the limitation of material label information is avoided. The image-based analysis mode is not limited by the types, materials or labels of the clothes, and can be suitable for various types of clothes, so that the processing efficiency is improved. According to the method, the area proportion of the white region and the fading possibility evaluation index of the non-white region (monitoring region) are comprehensively considered during analysis of the fading possibility of the specified clothes, so that the specified clothes are divided into clothes with fading possibility and clothes without fading possibility, and different types of specified clothes are cleaned respectively; and the overall fading risk of the clothes can be evaluated more finely.

Description

Technical Field

[0001] The invention belongs to the technical field of clothing recognition, and relates to a clothing recognition and analysis method of a washing robot based on image processing technology. Background Art

[0002] In the operation of the washing robot, clothing recognition is the key first step. Accurate clothing recognition enables the robot to select the appropriate washing mode, detergent dosage and washing time according to the characteristics of different clothes, such as color, degree of stains, etc., so as to achieve efficient, accurate and safe clothing washing. Therefore, the research on clothing recognition analysis of washing robots based on image processing technology is of great significance.

[0003] In the prior art, there are also related solutions for clothing recognition technology, for example, the Chinese invention patent application for intelligent washing and care method, device, system, equipment and readable storage medium with publication number CN117935277A, which includes: obtaining target image data corresponding to the target item group through the client. Obtaining item attribute information corresponding to the target item group based on the target image data through the processing end. The item attribute information includes target color information, target material information and target stain information. Determine the recommended washing mode corresponding to the target item group based on the target material information through the processing end, and determine the target washing parameters corresponding to the target item group based on the target color information and the target stain information. Wash the target item group based on the recommended washing mode and the target washing parameters through the device end.

[0004] In addition, a Chinese invention patent application for a method and device for identifying clothing materials with publication number CN113362269A includes: obtaining image information of clothing to be processed. According to a material recognition algorithm, performing image recognition on the image information to obtain actual material information of the clothing to be processed. Comparing the actual material information with preset standard material information corresponding to the clothing to be processed to obtain a material recognition result of the clothing to be processed. Pushing the material recognition result of the clothing to be processed to a user. The embodiment of the present invention can reduce the hardware cost of distinguishing the authenticity of clothing materials, simplify operations, and save time.

[0005] Although the above two schemes have proposed some solutions for clothing identification, they still have certain limitations: on the one hand, the existing technical solutions all identify the clothing labels and then determine the material and other data of the clothing. This analysis method cannot be applied to the identification of all clothing, which reduces the applicability of the system and affects the clothing identification efficiency of the system. On the other hand, the existing analysis of the clothing washing process after clothing identification uses the washing parameters corresponding to the clothing with the highest priority as the current washing parameters. This parameter setting method lacks refined classification analysis of clothing washing, which may cause over-washing of some clothing, affecting the washing effect, resulting in waste of resources and the accuracy of system analysis. Summary of the invention

[0006] In view of this, in order to solve the problems raised in the above background technology, a clothing recognition and analysis method of a washing robot based on image processing technology is proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: A method for identifying and analyzing clothing by a washing robot based on image processing technology, comprising: S1, acquiring images of designated clothing: using a high-definition camera to acquire images of each designated clothing, the images specifically including front images and back images.

[0008] S2. White area ratio analysis: analyzing the white area ratio of each designated clothing item based on the image of each designated clothing item.

[0009] S3. Analysis of the possibility of fading of designated clothing: Determine the monitoring area of ​​each designated clothing based on the image of each designated clothing, analyze the color consistency and shape irregularity evaluation of each designated clothing monitoring area corresponding to each adjacent non-white area, and then analyze the possibility of fading of each designated clothing.

[0010] S4. Classification of designated clothes: Classify each designated clothes based on the proportion of white area and the possibility of fading of each designated clothes to obtain the type of each designated clothes. The type of each designated clothes specifically includes clothes with the possibility of fading and clothes without the possibility of fading, and then wash them separately.

[0011] S5. Stain situation analysis: based on the image of each designated clothing, each stain area of ​​each designated clothing is located, and the stain evaluation situation of each stain area of ​​each designated clothing is analyzed, wherein the stain evaluation situation specifically includes stain color analysis and stain texture analysis.

[0012] S6. Stain type identification: Identify the stain type of each stain area of ​​each designated clothing based on the stain evaluation of each stain area of ​​each designated clothing, and then select the corresponding cleaning method and detergent for different types of stains, and then determine the cleaning method and detergent for different types of designated clothing.

[0013] Compared with the prior art, the present invention has the following beneficial effects: (1) When performing clothing identification, the present invention analyzes the possibility of discoloration of designated clothing based on the collected images, and then classifies the designated clothing. This analysis method can intuitively and accurately understand the actual condition of the clothing, avoiding the limitations of material label information. The image-based analysis method is not limited by clothing type, material or label, and can be applied to various types of clothing, including clothing without labels or with incomplete label information. It can achieve fast and accurate clothing identification and classification, greatly improving processing efficiency.

[0014] (2) When analyzing the fading possibility of designated clothing, the present invention comprehensively considers the white area ratio and the fading possibility evaluation index of the non-white area (monitoring area), and then divides each designated clothing into clothing with fading possibility and clothing without fading possibility, and performs washing operations for different types of designated clothing respectively. This analysis method can more accurately evaluate the overall fading risk of clothing, and different washing strategies can be adopted for different types of clothing to reduce the risk of staining, which helps protect the clothing and extend its service life.

[0015] (3) When washing designated clothes, the present invention identifies the stain type based on the stain color analysis and stain texture analysis results of each stained area of ​​each designated clothing. This analysis method accurately identifies the stain type based on the stain color and texture analysis. The washing robot can select the most suitable detergent and washing parameters for each stain, thereby improving the washing effect. Accurate stain type identification enables the washing robot to remove stains more effectively during a single washing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 It is a schematic diagram of the implementation steps of the method of the present invention.

[0018] Figure 2 A flow chart of an embodiment of designated clothing classification provided by the present invention.

[0019] Figure 3 A schematic diagram of an embodiment of stain type identification provided by the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1 As shown, the present invention provides a clothing recognition and analysis method for a washing robot based on image processing technology, including: S1, designated clothing image acquisition: using a high-definition camera to acquire images of each designated clothing, the images specifically including front images and back images.

[0022] It should be noted that during the image acquisition process, it is necessary to ensure that the designated clothing is as flat and unobstructed as possible.

[0023] S2. White area ratio analysis: analyzing the white area ratio of each designated clothing item based on the image of each designated clothing item.

[0024] In a preferred embodiment of the present invention, the specific analysis method of analyzing the proportion of white area of ​​each designated clothing is as follows: extract the front image and the back image of each designated clothing, and then use image processing software to obtain the white area area of ​​the front image, the white area area of ​​the back image, the front image clothing area and the back image clothing area corresponding to each designated clothing.

[0025] The white area ratio of the front image of each specified clothing item is calculated by dividing the white area of ​​the front image by the clothing area of ​​the front image; the white area ratio of the back image of each specified clothing item is calculated by dividing the white area of ​​the back image by the clothing area of ​​the back image to obtain the white area ratio of the back image of each specified clothing item.

[0026] The white area ratio of the front image and the white area ratio of the back image of each designated clothing are averaged to obtain the monitored white area ratio of each designated clothing.

[0027] S3. Analysis of the possibility of fading of designated clothing: Determine the monitoring area of ​​each designated clothing based on the image of each designated clothing, analyze the color consistency and shape irregularity evaluation of each designated clothing monitoring area corresponding to each adjacent non-white area, and then analyze the possibility of fading of each designated clothing.

[0028] In a preferred embodiment of the present invention, the analysis of the color consistency situation requires constructing a color consistency index for each designated clothing monitoring area corresponding to each adjacent non-white area, and the specific method is as follows: extract the front image and the back image of each designated clothing, and then use image processing software to obtain the area of ​​each non-white area of ​​each designated clothing, compare the area of ​​each non-white area of ​​each designated clothing, and select the non-white area corresponding to the largest area as the monitoring area.

[0029] It needs to be explained that when analyzing the color consistency index of each designated clothing monitoring area corresponding to each adjacent non-white area, the non-white area corresponding to the largest area is selected as the monitoring area for analysis. The reasons are: 1. It can more representatively reflect the overall color characteristics of the non-white part of the clothing. Since the color of large areas is usually more stable and less susceptible to local color differences, using it as the analysis object can more accurately evaluate the color consistency of clothing. 2. In actual operation, analyzing the color consistency index of multiple small areas may take a lot of time and effort. Selecting the area with the largest area for analysis can improve analysis efficiency and reduce operating costs while ensuring analysis accuracy.

[0030] The image processing software is used to obtain the chromaticity value of each designated clothing monitoring area and the chromaticity value of each corresponding adjacent non-white area.

[0031] The chromaticity value of each designated clothing monitoring area and the chromaticity value of each corresponding adjacent non-white area are compared, and the color consistency index of each designated clothing monitoring area and each adjacent non-white area is analyzed and obtained.

[0032] It should be added that the specific method of analyzing and obtaining the color consistency index of each designated clothing monitoring area and each adjacent non-white area is: if the chromaticity value of a designated clothing monitoring area is consistent with the chromaticity value of a corresponding adjacent non-white area, then the color consistency index of the designated clothing monitoring area and the adjacent non-white area is set to 1.

[0033] If the chromaticity value of a designated clothing monitoring area is inconsistent with the chromaticity value of a corresponding adjacent non-white area, the chromaticity value of the designated clothing monitoring area and the chromaticity value of the corresponding adjacent non-white area are difference calculated and the absolute value is taken to obtain the chromaticity value deviation between the designated clothing monitoring area and the adjacent non-white area, and then the pre-set reference chromaticity value deviation is respectively ratio-calculated with the chromaticity value deviation between the designated clothing monitoring area and the adjacent non-white area to obtain the color consistency index between the designated clothing monitoring area and the adjacent non-white area.

[0034] Then, the color consistency index between each designated clothing monitoring area and each adjacent non-white area is obtained.

[0035] It needs to be explained that the color consistency index of each designated clothing monitoring area and each adjacent non-white area reflects the color consistency of each designated clothing monitoring area and the color of the corresponding adjacent non-white area. In the chromaticity value analysis method, the colors corresponding to adjacent chromaticity values ​​are similar. The smaller the chromaticity value deviation, the closer the corresponding colors are. In practice, if clothing fades, the color of the color area may change. Therefore, the closer the chromaticity values ​​of adjacent color areas are, the higher the possibility of fading. Combined with other influencing indicators (such as shape irregularity for example), it is comprehensively judged whether the corresponding color area is a fading area. If the chromaticity value of a monitoring area is significantly different from the chromaticity value of the corresponding adjacent non-white area, it reflects that the color difference between the two areas is large (such as red and blue for example). In this case, the possibility of fading is low.

[0036] In a preferred embodiment of the present invention, the analysis of the shape irregularity evaluation situation requires the construction of a shape irregularity evaluation index corresponding to each adjacent non-white area of ​​each designated clothing monitoring area, and the specific method is as follows: using image processing software to obtain the area and perimeter of each adjacent non-white area corresponding to each designated clothing monitoring area, respectively recorded as , ,in Indicates the number of the specified clothing. , Indicates the number of specified clothes. Indicates the number of the adjacent non-white area, , Indicates the number of adjacent non-white areas.

[0037] Using the formula The shape irregularity evaluation index of each adjacent non-white area corresponding to each designated clothing monitoring area is obtained by analysis. ,in Indicates the preset reference area. Indicates a preset reference circumference.

[0038] It is necessary to explain why the area and perimeter of each adjacent non-white area corresponding to each designated clothing monitoring area are selected as the shape irregularity evaluation index: the shape irregularity evaluation index is a quantitative indicator used to measure the degree of deviation of an object shape from a regular shape (such as a circle, square, etc.). It can help us describe the complexity and irregularity of the shape of an object more objectively. In two-dimensional geometric analysis, there is a certain relationship between the regularity of a figure and the perimeter and area. Under the same area, the longer the perimeter, the more irregular the corresponding image.

[0039] In a preferred embodiment of the present invention, the analysis of the possibility of fading of each designated clothing item requires the construction of a fading possibility evaluation index for the monitoring area of ​​each designated clothing item, and the specific method is as follows: extracting the color consistency index and shape irregularity evaluation index of the monitoring area of ​​each designated clothing item and each adjacent non-white area, and then summing and calculating the weighted sum to obtain the fading possibility evaluation index for the monitoring area of ​​each designated clothing item.

[0040] Exemplarily, the corresponding weights of the color consistency index and the shape irregularity evaluation index of each monitoring area of ​​each designated clothing and each adjacent non-white area are 0.6 and 0.4.

[0041] It needs to be explained that the basis for setting the weights corresponding to the color consistency index and shape irregularity evaluation index of the monitoring area of ​​each designated clothing and each adjacent non-white area is: based on the comprehensive consideration of multiple factors such as the requirements for stain identification accuracy, color space characteristics and visual perception factors, the association between washing procedures and stain types, and the importance of stain shape characteristics, combination with clothing style structure, cleaning difficulty and targeted cleaning strategies, etc., aiming to accurately locate stains, distinguish stain types and determine appropriate cleaning methods, so as to improve the cleaning efficiency and quality of the washing robot.

[0042] S4. Classification of designated clothes: Classify each designated clothes based on the proportion of white area and the possibility of fading of each designated clothes to obtain the type of each designated clothes. The type of each designated clothes specifically includes clothes with the possibility of fading and clothes without the possibility of fading, and then wash them separately.

[0043] In a preferred embodiment of the present invention, the specific method of classifying each designated clothing is as follows: Figure 2 As shown, the monitored white area ratio and the monitored area fading possibility evaluation index of each designated clothing are extracted, and then compared with the preset monitored white area ratio threshold and the monitored area fading possibility evaluation index threshold, respectively.

[0044] Exemplarily, the threshold for monitoring the white area ratio is 0.8, and the threshold for the evaluation index of the possibility of color fading in the monitoring area is 0.85.

[0045] If the proportion of the monitored white area of ​​a specified clothing item is less than the threshold of the monitored white area proportion and the monitoring area fading possibility assessment index is greater than the threshold of the monitoring area fading possibility assessment index, the specified clothing item is judged to be clothing with the possibility of fading; otherwise, the specified clothing item is judged to be clothing with no possibility of fading.

[0046] When analyzing the possibility of discoloration of designated clothes, the present invention comprehensively considers the proportion of white area and the discoloration possibility evaluation index of non-white area (monitoring area), and then divides each designated clothes into clothes with discoloration possibility and clothes without discoloration possibility, and performs washing operations for different types of designated clothes respectively. This analysis method can more finely evaluate the overall discoloration risk of clothes, and different washing strategies can be adopted for different types of clothes to reduce the risk of staining, which helps to protect the clothes and extend their service life.

[0047] It should be noted that the present invention analyzes the possibility of discoloration of designated clothing based on the collected images when identifying clothing, and then classifies the designated clothing. This analysis method can intuitively and accurately understand the actual condition of the clothing, avoiding the limitations of material label information. The image-based analysis method is not limited by clothing type, material or label, and can be applied to various types of clothing, including clothing without labels or with incomplete label information, and can achieve fast and accurate clothing identification and classification, greatly improving processing efficiency.

[0048] S5. Stain situation analysis: based on the image of each designated clothing, each stain area of ​​each designated clothing is located, and the stain evaluation situation of each stain area of ​​each designated clothing is analyzed, wherein the stain evaluation situation specifically includes stain color analysis and stain texture analysis.

[0049] In a preferred embodiment of the present invention, the stain color analysis needs to analyze the stain chromaticity value of each stain area of ​​each designated clothing, and the specific method is as follows: extract the image of each stain area of ​​each designated clothing, use image processing software to obtain the chromaticity value of each color area corresponding to each stain area of ​​each designated clothing, construct the HSV color space of each color area corresponding to each stain area of ​​each designated clothing, and then obtain the saturation and brightness of each color area corresponding to each stain area of ​​each designated clothing, and calculate the mean of the saturation and brightness of each color area corresponding to each stain area of ​​each designated clothing to obtain the monitoring chromaticity evaluation index of each color area corresponding to each stain area of ​​each designated clothing.

[0050] The monitored chromaticity evaluation indexes of each color area corresponding to each stain area of ​​each designated clothing are compared, and the color area corresponding to the maximum monitored chromaticity evaluation index is selected as the characteristic color area of ​​the stain area of ​​the designated clothing, and the chromaticity value of the characteristic color area is obtained as the stain chromaticity value of the stain area of ​​the designated clothing.

[0051] It should be noted that the reason for selecting the color area corresponding to the maximum monitored chromaticity evaluation index as the characteristic color area of ​​the stain area of ​​the specified clothing is: 1. Saturation reflects the purity or brightness of the color. In stain analysis, the color area with higher saturation may represent the core part of the stain or the original stain that has not been diluted or mixed. 2. Brightness determines the brightness or dullness of the color. In stain analysis, changes in brightness values ​​may reflect the contrast between the stain and the surrounding environment, as well as the brightness or darkness of the stain.

[0052] It needs to be explained that in the HSV color space, a set of data is usually composed of three parameters: hue, saturation and brightness. For example, if there is a set of data in the HSV color space (H: 30°, S: 80%, V: 60%), the color represented by this set of data is a color with a hue of 30° (close to red but with a slight orange component), a saturation of 80% (brighter color), and a brightness of 60% (medium brightness).

[0053] In a preferred embodiment of the present invention, the stain texture analysis needs to analyze the stain diffusion index of each stain area of ​​each designated clothing, and the specific method is as follows: extract the image of each stain area of ​​each designated clothing, and identify the number of color areas corresponding to each stain area of ​​each designated clothing.

[0054] If the number of color regions corresponding to a stain region of a specified clothing is 1, it is determined that the stain diffusion index of the stain region of the specified clothing is 0.

[0055] It should be noted that if the number of color areas corresponding to a stain area of ​​a specified piece of clothing is 1, it indicates that diffusion has occurred in the stain area and the color of the stain area is relatively uniform.

[0056] If the number of color areas corresponding to a stain area on a specified clothing is greater than 1, the image processing software is used to obtain the innermost color area area and the outermost color area area corresponding to each stain area of ​​each specified clothing, and the stain diffusion index of each stain area is calculated by calculating the ratio of the outermost color area area and the innermost color area area corresponding to each stain area of ​​each specified clothing.

[0057] It should be noted that the method for obtaining the area of ​​the innermost color area and the area of ​​the outermost color area is as follows: obtain each stain area of ​​each designated clothing, and then divide them according to the color area to obtain each color area corresponding to each stain area of ​​each designated clothing, obtain the center point of the corresponding stain area, and then obtain the closest distance of each color area to the center point as the monitoring distance, compare the monitoring distances corresponding to each color area, select the color area corresponding to the minimum monitoring distance as the innermost color area, and select the color area corresponding to the maximum monitoring distance as the outermost color area, and then use image processing software to obtain the innermost color area area and the outermost color area area corresponding to each stain area of ​​each designated clothing.

[0058] S6. Stain type identification: Identify the stain type of each stain area of ​​each designated clothing based on the stain evaluation of each stain area of ​​each designated clothing, and then select the corresponding cleaning method and detergent for different types of stains, and then determine the cleaning method and detergent for different types of designated clothing.

[0059] In a preferred embodiment of the present invention, the method of identifying the stain type of each stain area of ​​each designated clothing is as follows: Figure 3 As shown, the stain chromaticity value and stain diffusion index of each stain area of ​​each designated clothing are extracted, and then compared with the reference chromaticity value range and reference stain diffusion index range corresponding to each pre-saved stain type, so as to identify the stain type of each stain area of ​​each designated clothing.

[0060] In a preferred embodiment of the present invention, the selection of corresponding cleaning methods and cleaning agents for different types of stains requires identifying the characteristic stain types of each specified clothing: extracting the stain type of each stain area of ​​each specified clothing, and then classifying them to obtain the stain area corresponding to each stain type of each specified clothing, using image processing software to obtain the area of ​​each stain area corresponding to each stain type of each specified clothing, and then performing cumulative calculation to obtain the stain area corresponding to each stain type of each specified clothing.

[0061] The stain areas corresponding to the stain types of the designated clothes are compared, and the stain type corresponding to the largest stain area is selected as the characteristic stain type of the designated clothes.

[0062] It should be noted that different types of stains require different cleaning agents and cleaning methods.

[0063] It needs to be further supplemented that the specific method of determining the cleaning method and cleaning agent for different types of designated clothes is as follows: extract the characteristic stain type corresponding to each designated clothes with the possibility of fading, and then count the number of each designated clothes with the possibility of fading corresponding to each characteristic stain type, and compare them, and take the stain type with the largest number of each designated clothes with the possibility of fading as the characteristic stain type of the clothes with the possibility of fading.

[0064] The characteristic stain types corresponding to the designated clothes with no possibility of fading are extracted, and then the number of designated clothes with no possibility of fading corresponding to each characteristic stain type is obtained by counting and comparing, and the stain type with the largest number of designated clothes with no possibility of fading is taken as the characteristic stain type of the clothes with no possibility of fading.

[0065] Select appropriate cleaning methods and cleaning agents based on the characteristic stain types of clothes that may fade and the characteristic stain types of clothes that may not fade.

[0066] It should be noted that the present invention identifies the stain type based on the stain color analysis and stain texture analysis results of each stain area of ​​each designated clothing when washing designated clothes. This analysis method accurately identifies the stain type based on the stain color and texture analysis. The washing robot can select the most suitable detergent and washing parameters for each stain, thereby improving the washing effect. Accurate stain type identification enables the washing robot to remove stains more effectively during a single washing process.

[0067] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A method for clothes recognition and analysis by a washing robot based on image processing technology, characterized in that: include: S1. Capturing images of designated clothing: using a high-definition camera to capture images of designated clothing, wherein the images specifically include front and back images; S2. White area ratio analysis: analyzing the white area ratio of each designated clothing based on the image of each designated clothing; S3, analyzing the possibility of color fading of designated clothes: determining the monitoring area of ​​each designated clothes based on the image of each designated clothes, analyzing the color consistency and shape irregularity evaluation of each adjacent non-white area corresponding to the monitoring area of ​​each designated clothes, and then analyzing the possibility of color fading of each designated clothes; S4, classifying designated clothes: classifying each designated clothes based on the proportion of white area and the possibility of fading of each designated clothes to obtain the type of each designated clothes, wherein the type of each designated clothes specifically includes clothes with the possibility of fading and clothes without the possibility of fading, and then washing them respectively; S5, stain situation analysis: based on the image of each designated clothing, each stain area of ​​each designated clothing is located, and the stain evaluation situation of each stain area of ​​each designated clothing is analyzed, and the stain evaluation situation specifically includes stain color analysis and stain texture analysis; S6. Stain type identification: Identify the stain type of each stain area of ​​each designated clothing based on the stain evaluation of each stain area of ​​each designated clothing, and then select the corresponding cleaning method and detergent for different types of stains, and then determine the cleaning method and detergent for different types of designated clothing.

2. The method for identifying and analyzing clothing by a washing robot based on image processing technology as claimed in claim 1, characterized in that: The specific analysis method for analyzing the proportion of the white area of ​​each designated clothing is as follows: Extracting the front image and the back image of each designated clothing, and then using image processing software to obtain the white area of ​​the front image, the white area of ​​the back image, the clothing area of ​​the front image, and the clothing area of ​​the back image corresponding to each designated clothing; The white area ratio of the front image of each designated clothing item is calculated by the ratio of the white area area of ​​the front image to the clothing area of ​​the front image, and the white area ratio of the back image of each designated clothing item is calculated by the ratio of the white area area of ​​the back image of each designated clothing item to the clothing area of ​​the back image, to obtain the white area ratio of the back image of each designated clothing item; The white area ratio of the front image and the white area ratio of the back image of each designated clothing are averaged to obtain the monitored white area ratio of each designated clothing.

3. The method for clothes recognition and analysis by a washing robot based on image processing technology as claimed in claim 2, characterized in that: The analysis of the color consistency situation requires constructing a color consistency index for each designated clothing monitoring area corresponding to each adjacent non-white area, and the specific method is as follows: Extract the front and back images of each designated clothing, and then use image processing software to obtain the area of ​​each non-white area of ​​each designated clothing, compare the areas of each non-white area of ​​each designated clothing, and select the non-white area corresponding to the largest area as the monitoring area; Using image processing software to obtain the chromaticity value of each designated clothing monitoring area and the chromaticity value of each corresponding adjacent non-white area; The chromaticity value of each designated clothing monitoring area and the chromaticity value of each corresponding adjacent non-white area are compared, and the color consistency index of each designated clothing monitoring area and each adjacent non-white area is analyzed and obtained.

4. The method for identifying and analyzing clothing by a washing robot based on image processing technology as claimed in claim 3, characterized in that: The analysis of the shape irregularity evaluation situation requires constructing a shape irregularity evaluation index corresponding to each adjacent non-white area of ​​each designated clothing monitoring area, and the specific method is as follows: The image processing software is used to obtain the area and perimeter of each adjacent non-white area corresponding to each designated clothing monitoring area, which are recorded as , ,in Indicates the number of the specified clothing. , Indicates the number of specified clothes. Indicates the number of the adjacent non-white area, , Indicates the number of adjacent non-white areas; Using the formula The shape irregularity evaluation index of each adjacent non-white area corresponding to each designated clothing monitoring area is obtained by analysis. ,in Indicates the preset reference area. Indicates a preset reference circumference.

5. The method for clothes recognition and analysis by a washing robot based on image processing technology as claimed in claim 4, characterized in that: The analysis of the fading possibility of each designated clothing item requires the construction of a fading possibility evaluation index for the monitoring area of ​​each designated clothing item, and the specific method is as follows: The color consistency index and shape irregularity evaluation index of the monitoring area of ​​each designated clothing and each adjacent non-white area are extracted, and then the color fading possibility evaluation index of the monitoring area of ​​each designated clothing is obtained by summing them up according to the weights.

6. The method for identifying and analyzing clothing by a washing robot based on image processing technology as claimed in claim 5, characterized in that: The specific method of classifying each designated clothing is as follows: Extracting the monitored white area ratio and the monitored area fading possibility evaluation index of each designated clothing, and then comparing them with the preset monitored white area ratio threshold and the monitored area fading possibility evaluation index threshold respectively; If the proportion of the monitored white area of ​​a specified clothing item is less than the threshold of the monitored white area proportion and the monitoring area fading possibility assessment index is greater than the threshold of the monitoring area fading possibility assessment index, the specified clothing item is judged to be clothing with the possibility of fading; otherwise, the specified clothing item is judged to be clothing with no possibility of fading.

7. The method for identifying and analyzing clothing by a washing robot based on image processing technology as claimed in claim 1, characterized in that: The stain color analysis requires analyzing the stain chromaticity value of each stain area of ​​each designated clothing, and the specific method is as follows: Extract the image of each stain area of ​​each designated clothing, use image processing software to obtain the chromaticity value of each color area corresponding to each stain area of ​​each designated clothing, construct the HSV color space of each color area corresponding to each stain area of ​​each designated clothing, and then obtain the saturation and brightness of each color area corresponding to each stain area of ​​each designated clothing, and calculate the mean of the saturation and brightness of each color area corresponding to each stain area of ​​each designated clothing to obtain the monitoring chromaticity evaluation index of each color area corresponding to each stain area of ​​each designated clothing; The monitored chromaticity evaluation indexes of each color area corresponding to each stain area of ​​each designated clothing are compared, and the color area corresponding to the maximum monitored chromaticity evaluation index is selected as the characteristic color area of ​​the stain area of ​​the designated clothing, and the chromaticity value of the characteristic color area is obtained as the stain chromaticity value of the stain area of ​​the designated clothing.

8. The method for clothes recognition and analysis by a washing robot based on image processing technology as claimed in claim 7, characterized in that: The stain texture analysis needs to analyze the stain diffusion index of each stain area of ​​each designated clothing, and the specific method is as follows: Extracting images of each stained area of ​​each designated clothing, and identifying the number of color areas corresponding to each stained area of ​​each designated clothing; If the number of color regions corresponding to a stain region of a specified clothing is 1, the stain diffusion index of the stain region of the specified clothing is judged to be 0; If the number of color areas corresponding to a stain area on a specified clothing is greater than 1, the image processing software is used to obtain the innermost color area area and the outermost color area area corresponding to each stain area of ​​each specified clothing, and the stain diffusion index of each stain area is calculated by calculating the ratio of the outermost color area area and the innermost color area area corresponding to each stain area of ​​each specified clothing.

9. The method for clothes recognition and analysis by a washing robot based on image processing technology as claimed in claim 8, characterized in that: The method of identifying the stain type of each stain area of ​​each designated clothing is as follows: The stain chromaticity value and stain diffusion index of each stain area of ​​each designated clothing are extracted, and then compared with the reference chromaticity value range and reference stain diffusion index range corresponding to each stain type stored in advance, so as to identify the stain type of each stain area of ​​each designated clothing.

10. The method for identifying and analyzing clothing by a washing robot based on image processing technology as claimed in claim 9, characterized in that: The selection of the corresponding cleaning method and cleaning agent for different types of stains requires identifying the characteristic stain type of each specified clothing: Extract the stain type of each stain area of ​​each designated clothing, and then classify it to obtain the stain area corresponding to each stain type of each designated clothing, use image processing software to obtain the area of ​​each stain area corresponding to each stain type of each designated clothing, and then perform cumulative calculation to obtain the stain area corresponding to each stain type of each designated clothing; The stain areas corresponding to the stain types of the designated clothes are compared, and the stain type corresponding to the largest stain area is selected as the characteristic stain type of the designated clothes.

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