Affiliations: College of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei, Anhui, China
Correspondence:
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Corresponding author: Song Chen, College of Mechanical and Electrical Engineering, Anhui Jianzhu University, Hefei, Anhui, China. E-mail: [email protected].
Abstract: Surface defect detection is critical for obtaining high-quality products. However, surface defect detection on circular tubes is more difficult than on flat plates because the surface of circular tubes reflect light, which result in missed defects. In this study, surface defects, including dents, bulges, foreign matter insertions, scratches, and cracks of circular aluminium tubes were detected using a novel faster region-based convolutional neural network (Faster RCNN) algorithm. The proposed Faster RCNN exhibited higher recognition speed and accuracy than RCNN did. Furthermore, incorporation of image enhancement in the method further enhanced recognition accuracy.
