Steel Surface Defects Detection Based on Deep Learning

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations


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.

Original languageEnglish
Title of host publicationAdvances in Physical Ergonomics and Human Factors - Proceedings of the AHFE 2018 International Conference on Physical Ergonomics and Human Factors, 2018
EditorsWaldemar Karwowski, Ravindra S. Goonetilleke
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319944838
StatePublished - 2019
EventAHFE International Conference on Physical Ergonomics and Human Factors, 2018 - Orlando, United States
Duration: 21 Jul 201825 Jul 2018

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357


ConferenceAHFE International Conference on Physical Ergonomics and Human Factors, 2018
Country/TerritoryUnited States


  • Defect detection
  • Fully convolutional networks
  • Wavelet transform


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