Defect detection on button surfaces with the weighted least-squares model
- 41 Downloads
Defect detection is important in quality assurance on production lines. This paper presents a fast machine-vision-based surface defect detection method using the weighted least-squares model. We assume that an inspection image can be regarded as a combination of a defect-free template image and a residual image. The defect-free template image is generated from training samples adaptively, and the residual image is the result of the subtraction between each inspection image and corresponding defect-free template image. In the weighted least-squares model, the residual error near the edge is suppressed to reduce the false alarms caused by spatial misalignment. Experiment results on different types of buttons show that the proposed method is robust to illumination vibration and rotation deviation and produces results that are better than those of two other methods.
Keywordsmachine vision surface defect detection weighted least-squares model
Unable to display preview. Download preview PDF.
- 4.Li W C, Tsai D M. Wavelet-based defect detection in solar wafer images with inhomogeneous texture. Pattern Recognition, 2012, 45(2): 742–756Google Scholar
- 17.Stauffer C, Grimson W E L. Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1999, 2: 246–252Google Scholar
- 18.Kaewtrakulpong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection.Springer US, 2002: 135–144Google Scholar
- 19.Zivkovic Z. Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of International Conference on Pattern Recognition, 2004Google Scholar