L. Xiao, T. Huang, B. Wu, Y. Hu and J. Zhou, "Surface Defect Detection using Hierarchical Features," 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), Vancouver, BC, Canada, 2019, pp. 1592-1596, doi: 10.1109/COASE.2019.8843235
Surface defect detection using hierarchical features
|Author:||Xiao, Ling1; Huang, Tao1; Wu, Bo1;|
1School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China
2ITEE -Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20201218101308
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-12-18
In this paper, we propose an instance level hierarchical features based convolution neural network model (H-CNN) for detecting surface defects. The H-CNN uses different convolutional layers’ extracted features to generate defect masks. The H-CNN first generates proposal regions. Then, it proposes a fully convolutional neural network to extract different level’s convolutional features and detect instance level defects. We applied the H-CNN model in freight train detection system for detecting oil-leaks, and the results demonstrate that the H-CNN can effectively identify and generate defect masks. It achieves 92% accuracy on the large reflective oil-leak stain, 86% on the large non-reflective oil-leak stain, 89% on the small reflective oil-leak stain and 74% on the small non-reflective oil-leak stain. Its image process speed is 0.467 s per frame.
IEEE International Conference on Automation Science and Engineering
|Pages:||1592 - 1596|
2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), August 22-26, 2019, Vancouver, BC, Canada
International Conference on Automation Science and Engineering
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
The research work was funded by the National Key Research and Development Program of China (No. 2017YFD0400400), the scholarship from China Scholarship Council(No. 201806160119).
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.