基于机器视觉的手机电池表面缺陷检测

针对手机电池表面质量人工检测情况,开发了电池表面缺陷无损检测系统软件。首先电池表面经过倾斜矫正、感兴趣区域提取和字符灰度值修改等预处理操作,通过基于灰度密度分布和灰度差的自适应阈值亮度法对感兴趣区域进行子图像遍历,融合有重合区域的缺陷子图像并滤除没有明显缺陷的区域;然后采用支持向量机多种类分类法,提取二值图像像素分布规律作为训练特征,识别电池表面缺陷种类;最后设计了人机交互界面,确定最佳的可变参数,实验测试缺陷识别率达95%以上。

Surface defect detection of mobile phone battery based on machine vision

Aiming at the current manual detection of mobile phone battery surface quality,a software program for non-destructive testing system for battery surface defects is designed.First,the surface of the battery is subjected to pre-processing operations such as tilt correction,ROI(Region Of Interest)extraction and character gray value modification.The adaptive threshold luminance based on gray density distribution and gray level difference is proposed to perform traversal of the sub-images of the ROI.The defective sub-images of the coincident regions are merged and the regions without obvious defects are filtered.Then,SVM(support vector machine)multi-class classification method is used to extract the binary image pixel distribution regularities as training feature and identify the battery surface defect types.Finally,the visual interface of the software is developed to determine the optimal variable parameters of the scheme.The recognition rate is as high as 95%by the experiment.