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.

SUBMITTER: Shu Y 

PROVIDER: S-EPMC8321889 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature