University of Oulu

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

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Author: Xiao, Ling1; Huang, Tao1; Wu, Bo1;
Organizations: 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
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.2 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
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.

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Series: IEEE International Conference on Automation Science and Engineering
ISSN: 2161-8070
ISSN-E: 2161-8089
ISSN-L: 2161-8070
ISBN: 978-1-7281-0356-3
ISBN Print: 978-1-72810-355-6
Pages: 1592 - 1596
Article number: 8843235
DOI: 10.1109/COASE.2019.8843235
Host publication: 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), August 22-26, 2019, Vancouver, BC, Canada
Conference: 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
Funding: 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).
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