Aimed at the problem of strong background interference introduced in digital image processing from complex surfaces under industrial defect detection, a method for complex surface defect detection based on human visual characteristics and feature extracting is proposed. Inspired by the visual attention mechanism, defect areas can be identified from the background noise conveniently by human eyes. We introduce the improved grayscale adjustment and frequency-tuned saliency algorithm combined with the salient region mask obtained by dilation and differential operation to eliminate the background noise and extract defect areas. Meanwhile the directional feature matching and merging algorithm is applied to enhance directional features and retain details of defects. Testing images are captured by our established detecting system. Experimental results show that our method can retain defect information completely and achieve considerable extracting efficiency and detecting accuracy.

Date Published: 7 March 2019
PDF: 8 pages
Proc. SPIE 11053, Tenth International Symposium on Precision Engineering Measurements and Instrumentation, 110531R (7 March 2019); doi: 10.1117/12.2511490

Published in SPIE Proceedings Vol. 11053:
Tenth International Symposium on Precision Engineering Measurements and Instrumentation
Jiubin Tan; Jie Lin, Editor(s)