Skin defect evaluation system and method based on image processing

Abstract

The invention discloses a skin defect evaluation system and method based on image processing, and belongs to the technical field of image data processing, the skin defect evaluation system comprises an image acquisition module, an extraction processing module and a feedback display module, the image acquisition module is responsible for acquiring and capturing skin images of a detected person in different time periods; the extraction processing module is responsible for operating calculation output of a preliminary evaluation value PC, a comprehensive skin evaluation value PJ and a dynamic skin evaluation value PJD by using a deep learning network and based on key features, and the feedback display module is responsible for displaying evaluation results of the preliminary evaluation value PC, the comprehensive skin evaluation value PJ and the dynamic skin evaluation value PJD. A comprehensive and detailed skin health evaluation system is formed, comprehensive, objective and dynamic evaluation of the skin condition is achieved, the severity of skin defects can be visually reflected, the overall attractiveness and health condition of the skin can be comprehensively reflected, and the health condition of the skin can be dynamically evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing, and particularly to a skin defect evaluation system and method based on image processing. Background Art

[0002] Skin defect evaluation systems mainly stem from people's increasing concern and demand for skin health. With the improvement of living standards and the diversification of beauty and skincare products, skin health has become a topic of widespread public concern. However, due to the complexity and diversity of skin problems, how to accurately and objectively evaluate skin conditions has become an urgent problem to be solved.

[0003] In traditional skin assessment methods, the system often only focuses on a single skin problem and lacks a comprehensive evaluation of the overall skin condition. In addition, existing assessment methods are mostly static assessments based on a certain point in time, unable to reflect the dynamic changes of skin conditions. Moreover, when quantifying skin features, the accuracy and efficiency are often limited, making it difficult to meet the high-precision and real-time assessment requirements. Summary of the Invention

[0004] The technical problem to be solved by the present invention is that there are drawbacks in the prior art such as lack of comprehensive evaluation, monitoring dynamics, and technical limitations. For this reason, we propose a skin defect evaluation system based on image processing.

[0005] The technical solution mainly is: A skin defect evaluation system based on image processing, including an image acquisition module, an extraction and processing module, and a feedback and display module; The image acquisition module is responsible for capturing skin images of the detected person at different time periods, and introducing artificial intelligence-assisted image quality evaluation to automatically detect and correct shadows, reflections, and external interference factors in the images; The extraction and processing module is responsible for using a deep learning network to automatically extract key features in the detected area and the overall skin area of the skin image, and based on the key features, calculating and outputting the preliminary evaluation value PC, the comprehensive skin evaluation value PJ, and the dynamic skin evaluation value PJD; The feedback and display module is responsible for displaying the evaluation results of the preliminary evaluation value PC, the comprehensive skin evaluation value PJ, and the dynamic skin evaluation value PJD; Among them, the extraction and processing module includes a skin preliminary defect evaluation unit, a comprehensive reflection skin unit, and a dynamic skin condition evaluation unit.

[0006] Preferably, the devices used by the image acquisition module include a high-definition camera and a skin detection instrument; The devices used by the extraction and processing module include a graphics processor, a high-performance computer, a server, and a neural network accelerator; The devices used by the feedback display module include a display.

[0007] Preferably, the calculation formula of the preliminary skin defect assessment unit is as follows: ; Where: PC is the preliminary evaluation value; A high PC value indicates more skin defects; A low PC value indicates fewer skin defects; QL is the amount of defective pixels, and QL reflects the amount of pixels of skin defects recognized by the image in the detection area; JM is the skin detection area, and JM reflects the total area of the skin in the detection area; QM is the defective coverage area, and QM reflects the size of the skin area with defects and covered within the total skin area JM in the detection area; ZM is the total skin area, and ZM reflects the total area of the skin in the detection area; The total skin area ZM includes the skin detection area JM, and the skin detection area JM includes the defective coverage area QM; On the basis of reflecting the density of skin defects, multiplied by reflects the degree of influence of the defects on the skin surface.

[0008] Preferably, the calculation formula of the unit comprehensively reflecting the skin is as follows: ; Where: PJ is the comprehensive skin evaluation value; A high PJ value indicates good overall skin condition; A low PJ value indicates poor overall skin condition; ZS is the overall pigmentation value, and ZS reflects the pigment content and concentration of the total skin area ZM; P is the brightness offset; JY is the pigmentation offset; The brightness offset P and the pigmentation offset JY are obtained by quantifying through image processing technology, specifically calculating the difference in brightness and pigmentation between different regions of the overall skin image and the detection area.

[0009] Preferably, the calculation formula of the unit for dynamically evaluating the skin condition is as follows: ; Where: PJD is the dynamic skin evaluation value; H is the average erythema intensity, which reflects the average intensity under the dynamic change of the red channel in the overall color during the skin image detection period; C is the degree of pigmentation, which reflects the average degree of the overall color during the skin image detection period; A high PJD value indicates a reduction in skin problems reflected by skin erythema and pigmentation; A low PJD value indicates an increase in skin problems reflected by skin erythema and pigmentation.

[0010] Preferably, the quantity QL of defective pixels, the skin detection area JM, the defective coverage area QM, the total skin area ZM, the overall pigmentation value ZS, the brightness offset P, the pigmentation offset JY, the average erythema intensity H, and the degree of pigmentation C are key features in the skin image.

[0011] A skin defect evaluation system based on image processing, and the technical solution achieved by using it is mainly: a skin defect evaluation method based on image processing, and the specific method steps include: S1: Use the image acquisition module to collect and capture the skin images of the detected person at different time periods, and introduce artificial intelligence algorithms for image quality evaluation and optimization; S2: After using the deep learning algorithm to denoise and enhance the image, then use the deep learning network to automatically extract the key features in the skin image; S3: Based on the key features, and use the artificial intelligence algorithm of the extraction and processing module to calculate and output the evaluation result; S4: Use the feedback and display module to display the evaluation result to the detected person.

[0012] Preferably, the skin defect evaluation method is executed by the image acquisition module, the extraction and processing module, and the feedback and display module.

[0013] The technical effects and advantages of the present invention: In the present invention, based on the technologies of image processing and artificial intelligence, the system can automatically and accurately extract the key features of the detected person at different time periods, and calculate according to the preset algorithm formula, so as to obtain objective and consistent evaluation results, which greatly reduces the influence of subjective factors on the evaluation accuracy. And due to the application of advanced image processing technologies and artificial intelligence algorithms, not only the accuracy and efficiency of skin feature quantification are improved, but also personalized evaluation can be carried out according to the individual differences of the detected person. The artificial intelligence algorithm can quickly process a large amount of skin image data, accurately identify and quantify various skin features, and provide more practical evaluation results for different individuals.

[0014] In the present invention, multiple dimensions such as the quantity of defective pixels QL of the skin image, the skin detection area JM, the defective coverage area QM, the total skin area ZM, the overall pigmentation value ZS, the brightness offset P, the pigmentation offset JY, the average erythema intensity H, and the degree of pigmentation C can be comprehensively considered. Based on this, through the preliminary skin defect evaluation unit, the skin unit can be comprehensively reflected, and the skin condition can be dynamically evaluated by the skin condition evaluation unit, realizing a comprehensive evaluation of the skin condition. Among them, artificial intelligence technology can deeply analyze and integrate these multi-dimensional data to provide a more comprehensive and accurate skin health assessment.

[0015] In addition, the dynamic skin evaluation value PJD of the dynamic skin condition evaluation unit particularly considers the dynamic changes of the skin condition. On the static basis of the comprehensive skin evaluation value PJ, it can combine the changing trends of the average erythema intensity H and the degree of pigmentation C in the skin image at different time periods. The system driven by artificial intelligence can monitor the changes of the skin condition in real time and issue warnings in a timely manner. When there are problems with erythema inflammatory reactions on the skin, the system can capture the changes in a timely manner and provide feedback, providing a basis for adjusting the skin care plan in a timely manner.

[0016] In the present invention, there is a close cyclic influence relationship between the dynamic skin evaluation value PJD and the preliminary evaluation value PC. The artificial intelligence technology supports the system to automatically update and analyze the data, adjust the evaluation parameters according to the dynamic changes of the skin. When the skin condition changes, the preliminary evaluation value PC will be adjusted accordingly to ensure the timeliness and accuracy of the evaluation results. This intelligent evaluation method improves the pertinence and effectiveness of skin care. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is the method flow chart of this skin defect evaluation method; Figure 2 is the overall structural schematic diagram of this skin defect evaluation system; Figure 3 is the distribution schematic diagram of the skin detection area JM, the defective coverage area QM, and the total skin area ZM in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Now, the present invention will be further described in detail with reference to the accompanying drawings and preferred embodiments.

[0019] Refer to Figures 1 to 3 As shown, the present invention provides a skin defect evaluation system based on image processing, including an image acquisition module, an extraction and processing module, and a feedback and display module.

[0020] The image acquisition module is responsible for capturing the skin images of the detected person at different time periods, and introducing artificial intelligence-assisted image quality evaluation to automatically detect and correct the shadow, reflection, and external interference factors in the images; The extraction and processing module is responsible for automatically extracting the key features in the skin image detection area and the overall skin area using a deep learning network, and based on the key features, calculating and outputting the preliminary evaluation value PC, the comprehensive skin evaluation value PJ, and the dynamic skin evaluation value PJD; The feedback and display module is responsible for displaying the evaluation results of the preliminary evaluation value PC, the comprehensive skin evaluation value PJ, and the dynamic skin evaluation value PJD; Among them, the extraction and processing module includes a preliminary skin defect assessment unit, a comprehensive reflection skin unit, and a dynamic skin condition evaluation unit; The number of defective pixels QL, the skin detection area JM, the defective coverage area QM, the total skin area ZM, the overall pigmentation value ZS, the brightness offset P, the pigmentation offset JY, the average erythema intensity H, and the pigmentation degree C are the key features in the skin image; The devices used by the image acquisition module include a high-definition camera and a skin detection instrument; The devices used by the extraction and processing module include a graphics processor, a high-performance computer, a server, and a neural network accelerator; The devices used by the feedback and display module include a monitor.

[0021] In this embodiment, the image acquisition module is responsible for capturing the skin images of the detected person at different time periods, and introducing artificial intelligence-assisted image quality evaluation. The high-definition camera and the skin detection instrument obtain high-quality skin images. The artificial intelligence algorithm automatically detects and corrects the shadows, reflections, and external interference factors in the images, providing an accurate data source for subsequent feature extraction and quantification. The extraction and processing module uses a deep learning network, supported by the artificial intelligence algorithm, to automatically extract the key features in the skin image detection area and the overall skin area. The graphics processor, high-performance computer, server, and neural network accelerator devices provide hardware support for the efficient operation of the algorithm. The deep learning network can quickly and accurately identify and extract the key features in the skin image, providing a basis for subsequent evaluation calculations; High-definition cameras and skin detection instruments are the basis for obtaining skin images. They can capture high-quality skin images, providing an accurate data source for subsequent feature extraction and quantification. High-performance computers and servers are responsible for running deep learning algorithms and image processing algorithms to process and analyze the collected skin images. High-performance computers and servers are usually equipped with powerful processors, large-capacity memory, and high-speed storage devices to ensure the efficient operation of algorithms and the rapid processing of data. In the field of deep learning, graphics processors play a crucial role. They have powerful parallel computing capabilities and can accelerate the training and inference processes of deep learning models. In the feature extraction and quantification steps, graphics processors can significantly improve the running speed of algorithms, thereby accelerating the analysis and processing of skin images. Neural network accelerators can be embedded in high-performance computers and servers or used as independent hardware modules to further improve the running efficiency of algorithms.

[0022] Referring to Figure 1 and Figure 3 shown, in this implementation: The calculation formula of the skin preliminary defect assessment unit is as follows: ; Where: PC is the preliminary evaluation value; A high PC value indicates more skin defects; A low PC value indicates fewer skin defects; QL is the quantity of defective pixels, and QL reflects the pixel quantity of skin defects recognized by the image in the detection area; JM is the skin detection area, and JM reflects the total area of the skin in the detection area; QM is the defective coverage area, and QM reflects the size of the skin area with defects and covered within the total skin area JM in the detection area; ZM is the total skin area, and ZM reflects the total skin area of the detection area; The total skin area ZM includes the skin detection area JM, and the skin detection area JM includes the defective coverage area QM; In On the basis of reflecting the density of skin defects, multiplying by reflects the degree of influence of the defects on the skin surface.

[0023] In this embodiment, The calculation part represents the number of defects per unit skin area, which helps to understand the density of skin defects. The more the number of defects, that is, the more the quantity of defective pixels QL, the larger this ratio, indicating more skin defects and a worse skin condition. The calculation part takes into account the proportion of the skin area covered by defects in the total area and performs a square root operation on it. The purpose of the square root operation is to adjust the impact of this proportion on the overall evaluation and avoid the rapid growth of the evaluation index caused by a large coverage ratio. This part reflects the degree of impact of the defects on the skin surface. Among them, since the defect pixel quantity QL exists in the skin detection area JM, therefore The calculation in the calculation part can better reflect the density of the defects and belongs to the local defect manifestation, while reflects the overall defect impact of the defects in the whole in terms of area; The preliminary skin evaluation value PC calculated by this preliminary skin defect evaluation unit is a quantitative index. In the calculation, the artificial intelligence algorithm automatically calculates the parameters of the defect pixel quantity QL, the skin detection area JM, the defect coverage area QM, and the total skin area ZM, eliminating the subjectivity in the traditional evaluation and making the evaluation result more objective and accurate. The final calculation result, the preliminary evaluation value PC, also provides basic data for the subsequent comprehensive evaluation and dynamic evaluation. Among the two parameters of the defect pixel quantity QL and the defect coverage area QM in the calculation, both are objectively quantifiable, so they can more truly reflect the actual situation of the skin. At the same time, the calculation result of the preliminary evaluation value PC can also provide basic data for the subsequent comprehensive evaluation and dynamic evaluation, ensuring the coherence and accuracy of the entire evaluation system. With the assistance of artificial intelligence technology, the calculation process of the preliminary evaluation value PC can be completed automatically and quickly, which not only improves the evaluation efficiency but also greatly enhances the evaluation accuracy.

[0024] Refer to Figure 1 As shown, in this implementation scheme: The calculation formula for comprehensively reflecting the skin unit is as follows: ; Among them: PJ is the comprehensive skin evaluation value; A high PJ value indicates a good overall skin condition; A low PJ value indicates a poor overall skin condition; ZS is the overall pigmentation value, and ZS reflects the pigment content and concentration of the total skin area ZM; P is the brightness offset; JY is the pigmentation offset; The brightness offset P and the pigmentation offset JY are quantified through image processing technology, and specifically calculate the difference degrees of brightness and pigmentation between different regions of the overall skin image and the detection region.

[0025] In the comprehensive skin unit of this embodiment, The calculation part adds the brightness offset P and the pigmentation offset JY. When considering the skin's health condition, both the pigmentation offset JY and the brightness offset P are important factors. The pigmentation offset JY reflects the deviation of pigment distribution in different areas of the skin, while the brightness offset P is related to the overall brightness and gloss of the skin. Artificial intelligence technology quantifies the brightness offset O and the pigmentation offset JY through image processing technology, conducts comprehensive quantitative analysis on multiple skin characteristics, and more comprehensively evaluates the overall health condition of the skin. Although the overall pigmentation value ZS itself already reflects the overall pigmentation situation, it is multiplied by The purpose of the calculation part is to further refine this evaluation when considering the local changes in pigmentation and the overall brightness of the skin. Brightness is an important part of the skin appearance and, together with pigmentation, affects the overall visual effect of the skin. Therefore, incorporating the brightness offset P into the calculation helps to more accurately reflect the actual condition of the skin. Subtracting the square root of the preliminary evaluation value PC is to reflect the impact of skin defects on the overall health index. Taking the square root is to adjust the influence degree of the preliminary evaluation value PC on the comprehensive skin evaluation value PJ and avoid excessive influence of an overly large preliminary evaluation value PC on the comprehensive skin evaluation value PJ; The comprehensive skin evaluation value PJ is a more comprehensive evaluation index. It not only considers the skin's pigmentation and brightness factors, but more importantly, by introducing the preliminary evaluation value PC, it comprehensively reflects the impact of skin defects on the overall aesthetics. In traditional skin evaluations, often only one aspect of skin problems is concerned, while the overall health condition of the skin is ignored. However, the design of the comprehensive skin evaluation value PJ is precisely to make up for this deficiency. It conducts comprehensive quantitative analysis on multiple skin characteristics, thereby more comprehensively evaluating the overall health condition of the skin. In the calculation process of the comprehensive skin evaluation value PJ, the skin's pigmentation and brightness are two important parameters. Pigmentation reflects the color uniformity and skin color depth of the skin, while brightness represents the gloss and transparency of the skin. The combination of these two parameters with the preliminary evaluation value PC enables the comprehensive skin evaluation value PJ to more accurately reflect the overall aesthetics and health condition of the skin. With the support of artificial intelligence technology, the calculation of the comprehensive skin evaluation value PJ can achieve automatic analysis of these characteristics.

[0026] Refer to Figure 1 As shown, in this implementation plan: The calculation formula of the dynamic skin condition evaluation unit is as follows: ; Where: PJD is the dynamic skin evaluation value; H is the average erythema intensity, and H reflects the average intensity under the dynamic change of the red channel in the overall color during the skin image detection period; C is the degree of pigmentation, which reflects the average degree of the overall color during the skin image detection period; A high PJD value indicates a reduction in skin problems reflected by skin erythema and pigmentation; A low PJD value indicates an increase in skin problems reflected by skin erythema and pigmentation.

[0027] In the unit for dynamically evaluating skin conditions of this embodiment, there are differences in the skin pigmentation degrees of different people, and directly comparing the erythema degrees is not accurate enough. Therefore, by using The calculation partially eliminates the influence of the skin pigmentation degree on the evaluation of the erythema degree, and thus obtains a relative erythema degree index. By dividing by the pigmentation degree C, the evaluation of the erythema degree can be standardized to make it more objective. For The score of the calculation result also emphasizes the importance of the erythema degree in the evaluation of skin health and increases its weight in the evaluation system, making it occupy a more important position in the final evaluation. It should be noted that the average erythema intensity H and the pigmentation degree C are respectively the average degrees of the erythema intensity and the pigmentation degree of the detected person at different time periods, and further reflect the dynamic change trend of the detected person. Let The result of the calculation part is more dynamically observable; Combining the average erythema degree index under the dynamic change trend with the overall health condition of the skin, a more comprehensive evaluation index is obtained. The comprehensive skin evaluation value PJ reflects the comprehensive performance of the skin in multiple aspects. Using it as the denominator can balance and integrate the influence of the erythema degree index, making the evaluation result more comprehensive. And for The subtraction operation in the calculation part is to obtain a difference value. Specifically, in the unit for dynamically evaluating skin conditions The calculation part represents a comprehensive index based on the average erythema degree index under the dynamic change trend and the overall health condition of the skin, while The calculation part represents the adjusted value considering skin defects. By subtracting, the relative importance of the influence of different factors on skin health, as well as the interaction and cancellation effects between them, can be highlighted. This calculation method helps to provide a more detailed and dynamic skin health evaluation index; The dynamic skin evaluation value PJD of this dynamic skin condition evaluation unit fully considers the dynamic changes of skin conditions, especially the influence of sensitive indicators of erythema degree. Through the analysis of skin images at different time periods, the artificial intelligence algorithm calculates the average erythema intensity H and the pigmentation degree C parameters, realizing the real-time monitoring of the dynamic health status of the skin. When the skin condition changes, the system can timely adjust the evaluation results and provide corresponding skin care suggestions. In traditional skin evaluations, only the static conditions of the skin are often concerned, while the dynamic changes of the skin conditions are ignored. The design of the dynamic skin evaluation value PJD is precisely to capture such dynamic changes. The dynamic skin evaluation value PJD realizes the real-time monitoring of the dynamic health status of the skin by introducing the comprehensive skin evaluation value PJ, the preliminary evaluation value PC, and other relevant parameters. The erythema degree is an important sensitive indicator, which reflects the inflammatory response and microcirculation status of the skin. When the skin is stimulated and infected, the erythema degree will increase significantly. At this time, the dynamic skin evaluation value PJD can timely capture this change and issue a warning. Driven by artificial intelligence, the calculation of the dynamic skin evaluation value PJD can achieve real-time monitoring and warning; There is a close cyclic influence relationship between the dynamic skin evaluation value PJD and the preliminary evaluation value PC. This design not only improves the continuity and accuracy of the evaluation, but also makes the entire evaluation system more intelligent and adaptive under the application of artificial intelligence. Specifically, the calculation result of the dynamic skin evaluation value PJD can be real-time fed back into the preliminary evaluation value PC. According to the dynamic changes of the skin, the quantity of defective pixels QL and the defective coverage area QM will be adjusted and fed back to the preliminary evaluation index PC. This means that when the skin condition changes, the preliminary evaluation value PC will be adjusted accordingly, thus ensuring the real-time and accuracy of the evaluation results. The realization of this cyclic influence mechanism benefits from the support of artificial intelligence technology. By automatically completing the update and analysis of data, the system can provide more personalized and accurate skin care suggestions. Specifically, when the skin of the detected person shows an erythema inflammatory response, the dynamic skin evaluation value PJD will, with the help of artificial intelligence technology, timely capture this change and feed it back into the preliminary evaluation value PC. The system will adjust the skin care plan according to the actual situation of the detected person and recommend corresponding treatment measures. This intelligent evaluation method not only improves the pertinence and effectiveness of skin care, but also brings a more convenient and efficient skin care experience to the detected person; Finally, the feedback display module is responsible for displaying the evaluation results of the preliminary evaluation value PC, the comprehensive skin evaluation value PJ, and the dynamic skin evaluation value PJD. The display intuitively shows the evaluation results to the detected person, facilitating their understanding of their skin conditions. The system supported by artificial intelligence technology can provide personalized skin care suggestions and treatment measures for the detected person according to the evaluation results, improving the pertinence and effectiveness of skin care.

[0028] It should be noted that any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall also fall within the protection scope of the present invention.

Claims

1. A skin defect evaluation system based on image processing, including an image acquisition module, an extraction and processing module, and a feedback display module, characterized in that: The image acquisition module is responsible for acquiring and capturing skin images of the subject at different time periods, and introducing artificial intelligence-assisted image quality assessment to automatically detect and correct shadows, reflections and external interference factors in the image; The extraction processing module is responsible for automatically extracting key features in the skin image detection area and the overall skin area using a deep learning network, and based on the key features, running the calculation output of the preliminary evaluation value PC, the comprehensive skin evaluation value PJ and the dynamic skin evaluation value PJD; The feedback display module is responsible for displaying the evaluation results of the preliminary evaluation value PC, the comprehensive skin evaluation value PJ and the dynamic skin evaluation value PJD; The extraction and processing module includes a preliminary skin defect assessment unit, a comprehensive skin reflection unit, and a dynamic skin condition assessment unit.

2. The skin defect assessment system based on image processing according to claim 1, characterized in that: The equipment used in the image acquisition module includes a high-definition camera and a skin detection instrument; The equipment used by the extraction processing module includes a graphics processor, a high-performance computer, a server, and a neural network accelerator; The equipment used by the feedback display module includes a display.

3. The skin defect assessment system based on image processing according to claim 1, characterized in that: The calculation formula of the skin preliminary defect assessment unit is as follows: ； in: PC is the preliminary evaluation value; A high PC value indicates more skin defects; A low PC value means fewer skin defects; QL is the number of defective pixels, which reflects the number of pixels of skin defects recognized by the image in the detection area; JM is the skin detection area, and JM reflects the total area of ​​skin in the detection area; QM is the defect coverage area, which reflects the size of the skin area with defects in the detection area and covered within the total skin area JM; ZM is the total skin area, which reflects the total skin area of ​​the detection area; The total skin area ZM includes the skin detection area JM, and the skin detection area JM includes the defect coverage area QM; exist Based on the density of skin defects, The multiplication reflects the extent to which the defect affects the skin surface.

4. The skin defect assessment system based on image processing according to claim 3, characterized in that: The calculation formula for the comprehensive reflection skin unit is as follows: ； in: PJ is the comprehensive skin evaluation value; A high PJ value indicates good overall skin condition; A low PJ value indicates poor overall skin condition; ZS is the overall pigmentation value, which reflects the pigment content and concentration of the total skin area ZM; P is the brightness offset; JY is the pigmentation offset; The brightness offset P and the pigmentation offset JY are quantified by image processing technology, specifically calculating the difference in brightness and pigmentation between different areas of the overall skin image and the detection area.

5. The skin defect assessment system based on image processing according to claim 4, characterized in that: The calculation formula of the dynamic skin condition evaluation unit is as follows: ； in: PJD is the dynamic skin evaluation value; H is the average intensity of erythema, and H reflects the average intensity of the red channel under dynamic changes in the overall color during the skin image detection period; C is the degree of pigmentation, which reflects the average degree of the overall color during the skin image detection period; A high PJD value means that skin problems reflected by skin erythema and pigmentation are reduced; A low PJD value indicates an increase in skin problems reflected by skin erythema and pigmentation.

6. The skin defect assessment system based on image processing according to claim 5, characterized in that: The defective pixel quantity QL, skin detection area JM, defect coverage area QM, total skin area ZM, overall pigmentation value ZS, brightness offset P, pigmentation offset JY, average erythema intensity H and pigmentation degree C are key features in skin images.

7. A skin defect evaluation method based on image processing, characterized in that: The specific steps include: S1: Use the image acquisition module to capture the skin images of the subject at different time periods, and introduce artificial intelligence algorithms to evaluate and optimize image quality; S2: After denoising and enhancing the image using a deep learning algorithm, the deep learning network is then used to automatically extract key features from the skin image; S3: Based on the key features, the artificial intelligence algorithm of the extraction processing module is used to calculate and output the evaluation results; S4: Use the feedback display module to display the evaluation results to the person being tested.

8. The skin defect assessment method based on image processing according to claim 7, characterized in that: The method steps S1-S4 are performed by the image acquisition module, the extraction and processing module, and the feedback display module of the skin defect assessment system based on image processing according to any one of claims 1-6.

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