Citation

Zhang C, Hu X, He J, Liu J, Hou N. China Saf. Sci. J. 2022; 32(6): 38-43.

Copyright

(Copyright © 2022, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2022.06.2563

PMID

unavailable

Abstract

In order to address classification difficulty of tread defects for small sample tasks, a classification model based on SimAM and SpinalNet was proposed. Firstly, feature maps of each category of original images were extracted from pre⁃trained networks. Secondly, class features with stronger representation of defect images were extracted by using SimAM under limited training samples, local and overall semantics of feature maps were correlated by utilizing SpinalNet to obtain a strong distinguishing representation of defect class features. Finally, strong discriminating representation features were inputted to softmax classifier with L2 regularization, and classification results were obtained. The research shows that the accuracy rate of evaluation index of small sample tasks was 68. 35% and 100%, respectively, which was better than current mainstream deep learning model. © PHYSOR 2022 China Safety Science Journal. All rights reserved.

Language: zh