基于机器视觉的包装袋缺陷检测算法研究与应用

Machine-Vision Based Defect Detection Algorithm for Packaging Bags

提出了一种基于机器视觉的包装袋缺陷检测方法。以冰棍包装袋缺陷检测为实例,提取了长度、宽度、面积、填充度和监测框与内部目标区域的位置关系5种特征值,经缺陷检测与分类,输出了连袋、外形尺寸错误、包装袋上有异物和包装版面移动4种缺陷类型。实验结果表明,算法缺陷识别成功率可达98.75%,满足生产过程对实时、快速、高精度的要求,已被应用于实际生产线,取得了良好效果。

A machine-vision based defect detection method for packaging bags is proposed. Considering the defect detection of ice-lolly bags as an example, five kinds of eigenvalues of length, width, area, filling degree, and location relationship between the monitoring frame and the internal target region are extracted. After defect detection and classification, the following four types of defects are outputted: continuous bags, dimension errors, foreign matters on packages, and motion of packaging layout. The experimental results demonstrate that the recognition success rate of the proposed algorithm can reach 98.75%, which meets the requirements of high speed, high precision, and real time in the production process. The algorithm has been applied to an actual production line and has achieved good results.