摘要
圆周面凹坑是圆柱型覆膜锂电池缺陷检测的重要指标之一。圆周面凹坑检测存在如下两个干扰因素,图像沿着周向和轴向亮度不均匀和圆周面存在油污。针对上述干扰,提出了一种基于机器视觉的解决方案。首先,沿着圆周面轴向提取灰度分布曲线;然后,采用自定义的对光照分布和油污不敏感的灰度差分模型计算灰度分布曲线上的突变,并根据圆周面反光特点选择了突变点的提取阈值,从而实现凹坑的检测。在自建图库SUT-BY上进行了测试,结果显示所提方法对凹坑检测的拒真率和认假率均为0%。实际测试结果表明亮度不均匀没有对凹坑提取产生影响,也没有因油污而产生误检。
The pit on the cylindrical surface is one of the important indicators of cylindrical coated lithium battery circumference surface defect detection. There are two interference factors in the detection of craters on the circumferential surface. The image has uneven brightness along the circumferential and axial directions, and oil contamination on the circumferential surface. To solve these problems, a method for pit defect detection based on machine vision is proposed. Firstly, the gray scale distribution curve is extracted along the axis of the circumference. Then, the gray level difference model is used to extract gray discontinuous points in gray distribution curve, which is not sensitive to light distribution and oil contamination. The extraction threshold of discontinuous points are determined according to the reflective feature of the circumferential surface. In this way, the pit defect detection is achieved. The algorithm is evaluated on the self-built image database SUT-BY. Experimental results show that false rejection rate and false accept rate are both 0 percent. The actual test results indicate the uneven brightness has no influence on pit extraction, and there is no false detection caused by oil pollution.
作者
苑玮琦
郭绍陶
Yuan Weiqi;Guo Shaotao(Computer Vision Group,Shenyang University of Technology,Shenyang 110870,China;Key Laboratory of Machine Vision,Liaoning Province,Shenyang 110870,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2020年第2期146-156,共11页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(61271365)项目资助.
关键词
灰度差分模型
灰度分布曲线
圆柱型覆膜锂电池
反光特点
凹坑
gray level difference model
gray level distribution curve
cylindrical coated lithium battery
reflection feature
pit
