Steel Surface Defects Detection Based on Deep Learning

Wei Yang Lin, Chih Yang Lin, Guan Shou Chen, Chao Yung Hsu

研究成果: 書貢獻/報告類型會議論文篇章同行評審

9 引文 斯高帕斯(Scopus)

摘要

Surface defects detection plays a significant role in quality enhancement in steel manufacturing. However, manual inspection of steel surface slows down the entire manufacturing process and is time consuming. Currently, many methods have been proposed for automatic defect detection on hot-rolled steel surfaces. These methods usually follow two steps: pre-processing and segmentation. The pre-processing step is intended to overcome the uneven illumination of images while the segmentation step generates a binary map to identify defects. This kind of method heavily depends on feature selection approaches, but the defect features are usually not easy to obtain. In this paper, we propose an automatic steel surface defects detection method based on deep learning. Two deep learning models for defect detection are evaluated. The experimental results show that the evaluated methods can detect steel surface defects more effectively and accurately than the traditional methods. This approach can be also applied to other industrial applications.

原文???core.languages.en_GB???
主出版物標題Advances in Physical Ergonomics and Human Factors - Proceedings of the AHFE 2018 International Conference on Physical Ergonomics and Human Factors, 2018
編輯Waldemar Karwowski, Ravindra S. Goonetilleke
發行者Springer Verlag
頁面141-149
頁數9
ISBN(列印)9783319944838
DOIs
出版狀態已出版 - 2019
事件AHFE International Conference on Physical Ergonomics and Human Factors, 2018 - Orlando, United States
持續時間: 21 7月 201825 7月 2018

出版系列

名字Advances in Intelligent Systems and Computing
789
ISSN(列印)2194-5357

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???event.eventtypes.event.conference???AHFE International Conference on Physical Ergonomics and Human Factors, 2018
國家/地區United States
城市Orlando
期間21/07/1825/07/18

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