数据线的虚焊是数据线焊接过程的常见缺陷,严重影响数据线的验收与后续使用。目前针对该类缺陷多采用目视检测方式进行判断,工作效率低下,漏检率较高。已有的机器视觉算法多针对金属表面的焊缝、孔洞等缺陷进行检测,很少有算法专门对数据线的焊接质量进行检测。据此,设计了一种基于机器视觉的数据线焊接质量检测方法,首先通过角点检测分割原始图像得到待检测区域图像,再利用彩色图像局部二值模式(LBPC)分割待检测区域;然后利用轮廓检测和形态学运算获取各区域轮廓,根据轮廓特征对焊接缺陷进行分类;最后利用支持向量机(SVM)进行分类统计。试验结果表明,所提检测方法缺陷分类准确率为96%,针对性强,操作简单,具有较高的实用性。

False welding of data lines is a common defect in the welding process of data lines, which seriously affects the acceptance and subsequent use of data lines. At present, visual inspection is often used to judge such defects, which results in low work efficiency and high missed detection rate. Existing algorithms were mostly used to detect defects such as welds and holes on metal surfaces. However, there are few specialized methods for detecting the welding quality of data lines. According to this, a method of data line welding quality detection based on machine vision was designed. First, the original image was segmented to obtain the image of the area to be detected by corner detection, and then the area to be detected was segmented by color image local binary patterns (LBP). Then, contour detection and morphological operations were used to obtain the contours of each region, and welding defects were classified based on contour features. Finally, support vector machine (SVM) was used for classification and statistics. The experimental results showed that the proposed detection method had a defect classification accuracy of 96%, strong pertinence, simple operation, and high practicality.