基于特征与形貌重构的轴件表面缺陷检测方法

Shaft surface defect detection method based on feature and morphology reconstruction

针对轴件表面缺陷机器视觉检测方法中的水渍残留误检率高和人工复检效率低问题,提出一种基于特征与形貌重构的轴件表面缺陷检测方法.对轴件工业高速线扫描图像进行预处理,基于改进的阀值迭代算法完成图像分割,通过去除背景、噪点和干扰提取缺陷图像.建立基于曲线簇包络轮廓的轴件表面缺陷特征模型,结合分割图像各连通域的面积、面积占比、粗短度训练逻辑回归分类器,对凹坑、裂纹和麻点等轴件表面典型缺陷进行识别,并结合图像深度信息进行缺陷形貌重构,消除水渍等伪缺陷,提高轴件表面缺陷检测鲁棒性.实验结果表明,所提出的轴件表面缺陷检测方法有效,具有较高的缺陷识别率和鲁棒性能,平均识别时间为3.69 s,缺陷轴加权识别率为98.86%,可以对3类典型缺陷和水渍进行准确识别.

A surface defect detection method based on feature and morphology reconstruction was proposed, aiming at the problems of high misdetection rate of water stain residue and low efficiency of manual re-inspection in machine vision detection of defects in shaft parts surface. The high-speed industry line scanning image of the shaft part was preprocessed, and image segmentation was completed based on the improved threshold iteration algorithm.Defect image was extracted by removing background, noise, and interference. A surface defect feature model of shaft parts based on envelope contour of curve cluster was established. Combining the area, the proportion of area and coarseness of each connected area in the segmented image, a logistic regression classifier was trained to recognize the typical surface defects of pits, cracks, and pits of shaft parts. Combining with image depth information,defect morphology reconstruction was carried out to eliminate pseudo-defects such as water stain, so as to improve the robustness of surface defect detection of shaft parts. The experimental results show that this method is effective for surface defect detection of shaft parts with high defect recognition rate and robustness. The proposed method has an average recognition time of 3.69 seconds and a weighted recognition rate of 98.86%, which can accurately identify three kinds of typical defects and pseudo-defects.