关键词: 轴件表面缺陷, 主成分分析, 集成迁移学习, YOLO V3目标检测算法

Abstract: Aiming at the problem of insufficient data source and low detection efficiency in shaft surface defect detection,a real-time detection method of shaft surface defect based on integrated transfer learning was proposed.Through the transfer learning of pictures in similar fields,the problem of insufficient data samples was solved,and the dependence on artificial large-scale annotation data was reduced.Principal component analysis was applied to reduce the dimension of defects and extract key feature vectors,and the feature space of the defects was established.To simplify the repeated calculation of distance measurement between domains,the spatial locations were used to solve the source domain of transfer learning.The feature extractor of source domain images was trained,and the network weight of the feature extractor was transferred to You Only Look Once Version 3 (YOLO V3) target detection model to complete the knowledge transfer in the similar field,moreover the real-time detection model of shaft surface defects under high-speed production was established.Experiments showed that the method had high accuracy and robustness in real-time detection of shaft parts in production site.The integrated algorithm model had a positive detection rate of more than 97% for all kinds of defects,and the variance of the average accuracy had been reduced by nearly 3 times.

Key words: shaft surface defects, principal component analysis, integrated transfer learning, YOLO V3 target detection algorithm