圆柱锂电池端面凹坑缺陷检测方法研究

A research on the detection method of pit on the cylindrical lithium battery end surface

端面凹坑是圆柱锂电池缺陷检测的重要指标之一。因为明暗对比度小的浅凹坑极易受金属表面上随机出现的亮点暗斑等强噪声的干扰,造成浅凹坑自动检测十分困难。为此,提出了一种解决方案:首先针对在单一光源角度下难以获取清晰的浅凹坑图像问题,采集6张凹坑在不同光源角度下的图像;其次采用时域平均和剔除异常值方法对6张图像进行融合得到基准面图像,并采用基于滑动窗口和奈奎斯特采样定理的空间滤波方法,减弱了信息强度较强的干扰噪声,再根据误差分析理论,提取灰度分布曲线的平均偏差;然后根据凹坑在灰度分布曲线中的形态,提取凹凸曲线段峰谷差和宽度比;最后采用BP神经网络方法建立检测模型来实现凹坑检测。对现场采集到的样本进行了测试,算法的正确检测率为100%。

The end pit is one of the important indexes for defect detection of the cylindrical lithium battery.It is very difficult to detect shallow pits automatically because the shallow pits with small contrast are easily interfered by strong noise such as bright spots and dark spots appearing randomly on metal surface.Therefore,a solution is proposed in this article.Firstly,to obtain a clear shallow pit image under a single light source angle,the six images of pit under different light source angles are collected.Secondly,the temporal averaging and outlier elimination method are used to fuse six images to obtain the datum image,and the spatial filtering method based on sliding window and Nyquist sampling theorem is utilized to weaken the interference noise with strong information intensity.Then,the average deviation is calculated according to the error analysis theory.According to the shape of pits in the gray distribution curve,the peak-to-valley difference and width ratio of concave-convex curve segment are extracted.Finally,the BP neural network is used to formulate a detection model to realize pit detection.The samples collected on site are tested,and the correct detection rate of the algorithm is 100%.