Surface defect detection using hierarchical features
Xiao, Ling; Huang, Tao; Wu, Bo; Zhou, Jiehan (2019-09-19)
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
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https://urn.fi/URN:NBN:fi-fe20201218101308
Tiivistelmä
Abstract
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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