Current methods have limitations in detecting the uneven brightness and low contrast micro defects on the surface of the lithium battery electrode. To solve this problem, a algorithm based on the fusion of wavelet enhancement and the Canny operator was proposed. Firstly, the K-nearest mean filter algorithm was introduced to suppress the image background noise. Afterward, wavelet transform was implemented to separate the low-frequency and high-frequency components of the image. Subsequently, linear adjustment was adopted to process the low-frequency components, while the multi-scale detail enhancement method was used to process the high-frequency. Then PSO-OTSU adaptive algorithm was used to obtain the best threshold of the enhanced images. Finally, the Hough test was performed to connect the edge points. Through test defects such as leakage of metal, bright spots, scratches, holes each 700 images, the accuracy of quantitative analysis and comparison of 3 kinds of algorithm，experimental results show that compared with other algorithms, this algorithm had a detection accuracy of 97.85% and better performance in retaining the details of the defect edge, detecting low-contrast and micro defects, and extracting the defect contour.