基于机器视觉的极柱焊点定位算法研究

在锂电池包生产时, 极柱焊点的准确定位和高的定位精度及速度对于焊接质量非常重要。文章提出一种基于机器视觉的电池模组极柱焊点定位算法。首先, 对采集到的图像进行灰度化、双边滤波去噪、Gamma矫正、自适应二值化、形态学操作等预处理操作; 然后, 通过改进的 Mean-Shift 聚类算法筛选并分离矩形与圆轮廓, 经亚像素处理后采用随机抽样一致性(random sample consensus, RANSAC)算法拟合 4 条直线, 并采用基于改进的圆心聚类算法拟合圆; 最后, 使用得到的直线方程和圆方程计算出焊点坐标。实验结果表明, 文章算法定位速度快、精度高, 能够满足实际工业生产的要求。

In the production of lithium battery packs, the accurate positioning and high positioning accuracy and speed of the pole weld points are crucial for the quality of the welding. This paper proposes an algorithm for the positioning of battery module pole weld points based on machine vision. Firstly, the acquired images were preprocessed by grayscale, bilateral filtering and denoising, gamma correction, adaptive binarization and morphological operations. Then, rectangle and circular contours were filtered and separated through an improved Mean-Shift clustering algorithm. After sub-pixel processing, the random sample consensus(RANSAC) algorithm was used to fit four straight lines, and an improved center-based clustering algorithm was used to fit circles. Finally, the weld point coordinates were calculated by using the obtained linear and circular equations. The experiments have demonstrated that the algorithm possesses high positioning accuracy and speed, which satisfies the requirements of industrial production.