Research on precision visual inspection technology based on new energy battery manufacturing

Abstract

In recent years, the lithium battery industry has been developing rapidly, and in the process of its large-scale industrialized production, the automatic defect detection technology based on machine vision has extremely important research value. Because of the complexity of the lithium battery production environment, the defect morphology is variable, the current research results for lithium battery pole piece defect detection is relatively small. In order to meet the needs of lithium battery pole piece defect detection speed and accuracy, to solve the problems of complex background noise, defects and low contrast in the pole piece image, this paper proposes a lithium battery pole piece defect detection algorithm based on machine vision technology, firstly, adopt the topological mapping based on the weighted average neighborhood closure curve filtering template for the image noise reduction processing, and then use the wavelet transform based on the multiscale detail enhancement method for image enhancement processing;; subsequently, adopt the multi-scale detail enhancement method based on wavelet transform for image enhancement processing; and subsequently, use the topological mapping based on the weighted average neighborhood closure curve for image enhancement processing. Then, in order to solve the problem of uneven illumination and more speckle impurities in the polar film image, the area growth method is used and combined with differential geometry tools to extract the defect contour of the area to be tested; finally, the concept of Earth Move Distance (EMD) is introduced, which is used to compute the similarity between the obtained contour and various types of defect templates contours to realize the classification of defects. Experiments have shown that the algorithm in this paper improves the speed and accuracy of defect detection on the surface of the pole piece, retains the details of the defect edges, detects small defects with low contrast, and extracts the complete defect contour, which better meets the actual needs of industrial production.