Automatic Weld Defect Detection in Real-time X-ray Images Based on Support Vector Machine

摘要：

Automatic weld defect detection based on real-time Xray image plays a vital role in improving the automation level of radiographic inspection in industry. Most of the existing realtime automatic inspection technologies only use defect segmentation algorithms, which leads to the difficulty of reducing both the undetected rate and false alarm rate. In this paper, an effective method based on Support Vector Machine (SVM) is proposed to detect weld defect in real-time X-ray images. Firstly, all potential defects are segmented by background subtraction algorithm. Then three features including defect area, average grayscale difference to its surrounding district and grayscale standard deviation are extracted. Lastly, the extracted features are used as input to SVM classifier to distinguish nondefects from defects. Results show that the proposed automatic defect detection method can reduce the undetected rate and false alarm rate effectively in real-time X-ray images of weld.

关键词： Real-time X-ray image Defect detection Background subtraction Support Vector Machine

作者: Jiaxin Shao Han Shi Dong Du Li Wang Huayong Cao

作者单位: Department of Mechanical Engineering Tsinghua University Beijing, China Department of Production Management Julong Steel Pipe Co., Ltd Qingxian, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 1873-1877

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）