A Method for Identifying Defects on Highly Reflective Roller Surface Based on Image Library Matching

Abstract

Identification of the highly reflective surface defects on roller parts is a requirement to assure high quality of parts. However, the highly reflective roller surface has the reflection characteristics, which easily lead to the missed detection or wrong detection of defect targets in the visual recognition process. For this problem, a new identification method based on image library matching is proposed. First, to protect the edge information of the defect target while eliminating noise, preprocessing of the measured initial images can be accomplished by using the accelerated optimized bilateral filtering. Second, the entropy and grid gray gradient are used to achieve rough segmentation of highly reflective surface defects on roller parts. Finally, a defect fine identification method based on the Hu invariant moment matching integrated with morphological classification is proposed for achieving image library matching and further quickly removing the pseudodefects. Experimental tests were conducted to verify the effectiveness of the proposed method in achieving accurate identification of highly reflective surface defects on roller parts. The proposed method has an accuracy of 98.2%, and the running time can basically meet the requirements of real-time performance.