关键词: 机器视觉, 焊缝缺陷检测, 焊缝缺陷类型识别, 混合高斯模型, 背景差分法, 波形检测法

Abstract: In order to effectively identify and classify weld defects of thin-walled metal canisters in industrial production, a weld defect detection and classification algorithm based on machine vision is proposed in this paper. By using the Gaussian mixture model, a modified background subtraction method is proposed to extract the feature areas of the weld defects. On this basis, it proposes an algorithm for weld detection and classification according to the extracted features, such as the defect areas, the defect brightness and the gray-value curves. Experimental results show that the proposed algorithms can identify and classify the thin-walled weld defects with more than 96% of accuracy rate and can meet the requirement of the real-time and continuous weld defect detection.

Key words: machine vision, weld defect detection, weld defect classification, Gaussian mixture model, background subtraction, curve detection method