摘要
针对薄壁金属制罐焊缝的缺陷类型,提出两种基于机器视觉的焊缝缺陷检测方法,一种是累积灰度值波形分析法,另一种是帧差法。累积灰度值检测方法主要基于统计学原理,对焊缝部分所表现出来的统计学特征进行分析来判断焊缝是否存在缺陷;帧差法主要是根据连续两帧之差来检测是否存在焊缝缺陷。最后,通过使用Dempster-Shafer(D-S)证据理论来减少误判和漏判。实验结果表明,两种检测方法的结合使用,达到了90%以上的准确度。同时,由于算法计算量不高,对于检测的实时性要求也能得到满足。
For classified canister welding seam defects,we use the vision feathers and propose two methods for the real-timely detecting defects.In the proposed approaches,the region of the welding seam is aligned after a preprocessing procedure to the acquired images.The first method is named as the column gray-level accumulation inspection （CAI）.In this method,an original curve is shaped by implementing the accumulating operation and followed by being exerted the mean value smoothing operation.Then a modified first difference method is used for the curve in order to segment the defects of the image of the welding seam.The second method,named as the frame difference testing （FDT）,is proposed for defect detecting.Finally,an information fusion approach based on the D-S evidence theory with the CAI and FDT methods is used to reduce the rate of the false alarm and to improve the reliability of the defect detection.Experimental results show that the proposed method can detect the welding seam defects with 90 percent accuracy and can meet the requirement of real-timely continuous detection.
出处
《江南大学学报（自然科学版）》
CAS
2014年第4期387-392,共6页
Joural of Jiangnan University (Natural Science Edition)
基金
国家自然科学基金项目(61170119
61311130141)
江苏省自然科学基金项目(BK2010143)
