表面检测中微小缺陷的识别

基金项目:

长江学者和创新团队发展计划基金资助(IRT0423)

详细信息

Identifying Small Defects in Surface Inspection

摘要: 在表面缺陷检测中，针对光照不均或有纹理的产品上微小缺陷难于识别的问题，提出了一种新的视觉识别方法。该方法首先计算产品表面图像中每行和每列的灰度标准差，然后根据标准差的相对变化量判别缺陷，并确定缺陷的坐标。实验结果表明，该方法能准确识别和定位产品表面的微小缺陷。
- 表面检测 /
- 微小缺陷 /
- 视觉识别
Abstract: In surface defect inspection, a novel approach was introduced for identifying small defects on the object with non-uniform illumination or homogeneous texture. Firstly, it was to calculate the grey-level standard deviations of each row and each column in the surface image. Secondly, based on the relative variance of standard deviation, it could be confirmed whether defects were occurring or not, and the coordinate of the defect might be fixed on too. Experimental results indicated that the small defect in surface image of product could be identified accurately by this method.
- Surface inspection /
- Small defect /
- Vision identifying

[1]	Otsu N A. Threshold selection method form gray level histogram[J]. IEEE Trans on Syst Man Cybern,1979,9(1):61-66.
[2]	Kittler J Illingworth J. Minimum error threshold[J]. Pattern Recognition,1986,19(1):41-47.
[3]	岳贤军.产品表面图像中的缺陷自动检测方法研究[J].微计算机信息,2007,23(6):297-299.
[4]	杨治明，王晓蓉，陈应祖.基于BP人工神经网络图像分割技术[J].计算机应用,2006,26(12):145-146.
[5]	Jia H B, Yi L M, Shi J J. An intelligent real-time vision system for surface defect detection[C]. Proceedings of the 17th International Conference on Pattern Recognition, Cambridge,2004:239-242.
[6]	陆懿，陈光梦，程松.一种基于模糊集的灰度图像阈值分割算法[J].计算机工程与应用,2007,43(20):75-77.