基于Gamma矫正与LOG算法融合的锂电池极片缺陷检测方法

Lithium Battery Polar Piece Defect Detection Method Based on Gamma Correction and LOG Algorithm Fusion

针对锂电池极片在生产过程中缺陷检测效率低、漏检率高的问题,提出了一种基于Gamma矫正与LOG算法融合的锂电池极片缺陷检测方法。首先利用ROI提取和阈值二值化对缺陷图像进行预处理,然后采用对比度调整和Gamma矫正增强缺陷特征,再利用LOG算法分割缺陷区域,最后通过形态学中的先闭后开方法和Sobel算子实现缺陷检测。实验结果表明本文方法可以对锂电池极片压痕、破损、漏金属、气泡、黑斑等多种缺陷进行较为精确地检测,对实现锂电池极片缺陷的自动化检测具有重要意义。

In view of the low efficiency of defect detection and high leakage detection rate in the production process of lithium battery polar slice,a lithium battery pole pieces defect detection method based on the fusion of Gamma correction and LOG algorithm is proposed.First,the defect image is pre-processed by the ROI extraction and threshold binarization,then the defect feature is enhanced by contrast adjustment and gamma correction,and then the defect area is divided by LOG algorithm,and finally the defect detection is realized by the morphological first closed then open method and Sobel operator.The experimental results show that the method in this paper can accurately detect the various defects such as lithium battery polar pieces indentation,breakage,leakage metal,bubbles,black spots and so on,which is of great significance for the automatic detection of lithium battery polar pieces defects.