Surface Defect Detection and Recognition Method for Multi-Scale Commutator Based on Deep Transfer Learning

Abstract

In view of the fact that traditional strip surface defect detection and recognition methods cannot adapt to the changing actual detection environment, and deep learning-based detection and recognition methods have high requirements for data volume, a new strip surface defect detection and recognition based on deep transfer learning is proposed. Method: First, the ResNet network trained based on the ImageNet dataset is transferred to the Faster R-CNN classic target detection algorithm. In order to deal with the problem of large differences in defect scales, the regional recommendation network in Faster R-CNN is improved and designed. A multi-scale regional recommendation network (MS-RPN) is proposed. The strip surface defect data set is used for experimental verification. The experimental results show that compared with Faster R-CNN, the proposed method has higher accuracy and is more suitable for strip surface defect detection applications. The proposed method has an accuracy of 84.14%, 88.81%, 88.35%, 92.86%, 92.86% and 92.53 for detecting scratches, bruises, cracks, oil stains and black spots, respectively.