Abstract

The image quality degradation caused by noise makes the automatic optical inspection of surface defects difficult. This paper develops a method based on thresholding segmentation to detect the surface defects in a glass substrate. Traditional Otsu segmentation has poor anti-noise ability. In order to improve the traditional Otsu method, a straight-line intercept histogram is established directly from the two-dimensional information of an image, and then Otsu criteria can be used to find the best intercept threshold from the one-dimensional histogram established. The improved Otsu algorithm not only is simpler than the two-dimensional Otsu methods, but also has a robust anti-noise ability. In the surface defect detection, the contrast feature between object and background is simply extracted after the segmentation based on the improved Otsu method, and surface defects can be decided by the threshold of the contrast feature. The data used in the experiments include the surface images acquired by a line-scan CCD camera. The experimental results demonstrate that the proposed method is effective and computationally efficient.

© 2015 Optical Society of America

Steel-surface defect detection using a switching-lighting scheme

Yong-Ju Jeon, Doo-chul Choi, Sang Jun Lee, Jong Pil Yun, and Sang Woo Kim
Appl. Opt. 55(1) 47-57 (2016)

Automatic optical inspection platform for real-time surface defects detection on plane optical components based on semantic segmentation

Jules Karangwa, Linghua Kong, Dingrong Yi, and Jishi Zheng
Appl. Opt. 60(19) 5496-5506 (2021)

Unsupervised defect detection in textiles based on Fourier analysis and wavelet shrinkage

Guang-Hua Hu, Qing-Hui Wang, and Guo-Hui Zhang
Appl. Opt. 54(10) 2963-2980 (2015)

Machine vision system based on a coupled image segmentation algorithm for surface-defect detection of a Si₃N₄ bearing roller

Dahai Liao, Mingshuai Yin, Hongbin Luo, Jun Li, and Nanxing Wu
J. Opt. Soc. Am. A 39(4) 571-579 (2022)

Defect detection of capacitive touch panel using a nonnegative matrix factorization and tolerance model

Changcheng Jiang, Yanming Quan, and Xingui Lin
Appl. Opt. 55(9) 2331-2338 (2016)