关键词: 缺陷, 自动检测, 电池, Harris角点

Abstract: Equipment failure often brings some defects on the surface of batteries in the battery production process. The traditional artificial detection has weakness on the timeliness and durability. But there has not been any efficient automatic detection means for the ordinary battery by now. Concerning the distribution and morphological characteristics of the defects, a new automatic optical detection method based on computer vision was proposed. The proposed method used Canny operator and virtual granule collision method with the minimum value searching method to determine the area to be detected based on the battery anode surface morphology features. Considering the sharpness of the defect, Harris corner points were used to mark the defects as mark points. False mark points were filtered by the degree of aggregation of the points. The defect region would be extracted at last according to the location of mark points. The experimental results illustrate the detection success rate of the proposed method is over 90% and the method can work more efficiently than the popular wavelet analytical method. The study achievement provides a reference for product quality automatic detection on battery production.

Key words: defect, automatic detection, battery, Harris corner