Baru-Net: Surface Defects Detection of Highly Reflective Chrome-Plated Appearance Parts

Abstract

Chrome plating parts with highly reflective characteristics are often used as appearance parts and must undergo strict defect detection to ensure quality. The defect detection method based on machine vision is the best choice. But due to the characteristic of high reflection, image acquisition is hard. For diverse defect appearances, it is difficult to use traditional algorithm for feature extraction. In this paper, a reasonable lighting scheme was designed to collect images effectively, and artificial defect images were made to expand the dataset to improve the deficiency of defect samples. A network, Baru-Net (Bis-Attention Rule), based on Unet architecture, the CBAM module and the ASPP module, was designed, and a block-step training strategy was proposed. With hyperparameter debugging, the semantic segmentation and classification of defects were carried out, and an accuracy rate of 98.3% achieved. Finally, QT realized the call to the weight model so that the AI model could be integrated into the automatic detection system.