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The resistor color code is a crucial electronic component in circuit boards.To ensure quality,each link must be monitored to ensure correct resistance is installed.Different resistance values are labeled as corresponding classes due to the definite relationship between resistance values and the color arrangement of rings.This simplifies the problem,as resistance measurement becomes a matter of classification.This thesis discusses methods for resistance identification under different scenarios and conducts a thorough study.（1）Firstly,a color ring resistance recognition method based on template matching is proposed for capturing a color ring resistance image from a circuit board.The resistor’s color feature is used to segment it in HSV space.Then the resistor’s main region is located using morphological processing and the minimum bounding rectangle method.The color ring’s location is then identified using template matching,and the color ring color is determined by comparing pixel values.This method can efficiently and accurately recognize single resistance images in simple backgrounds and has great application potential.（2）However,these methods require high image quality and have poor robustness for complex multi-scene resistance in practical applications.To solve this issue,a lightweight color ring resistance identification method based on improved Mobile Net V3 is proposed.By creating a color ring resistance dataset containing 20 categories,the research content is transformed from the problem of resistance identification to the classification of single resistance image.Data Enhancement,CBAM attention module,optimizing classification layer,and adding jump link are adopted to improve the model’s accuracy.This method can quickly and accurately classify color ring resistors under different backgrounds,attitudes,and illumination conditions and has good practical value.（3）In the third part,a color ring resistance class recognition method based on improved YOLOV7 is proposed.This method redesigns the multi-scale detection structure to improve detection performance.The data enhancement method of copy and paste,FFM structure,and Focal loss were used to improve the detection rate.The experimental results show that the m AP of the modified YOLOV7 method is 95.1%,which is 20.5% higher than that of the original method.Overall,The findings of this thesis demonstrate that color ring resistors can be identified quickly and accurately across different scenarios,providing technical references for automated identification of color ring resistors.Future work will optimize the method to adapt to more diverse application scenarios while improving the accuracy and efficiency of resistance identification.