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- W2890939466 abstract "A Deep Residual Network with Transfer Learning for Pixel-level Road Crack Detection Seongdeok Bang, Somin Park, Hongjo Kim, Yeo-San Yoon and Hyoungkwan Kim Pages 753-756 (2018 Proceedings of the 35th ISARC, Berlin, Germany, ISBN 978-3-00-060855-1, ISSN 2413-5844) Abstract: Image-based crack detection methods have been extensively studied due to their cost-effectiveness in terms of data acquisition and processing. However, automated crack detection still remain a challenging task due to complexity of image background and different patterns of cracks. To address these issues, this paper proposes a deep residual network with transfer learning for pixel-level crack detection on road surface images. The network was trained on 71 images of CrackForest dataset and tested on 47 images of it. Experimental results suggest that the deep residual network is superior to the existing algorithms with recall value and precision value of 84.90% and 93.57%, respectively. Keywords: Crack Detection, Residual Network, Deep learning, Transfer Leaning DOI: https://doi.org/10.22260/ISARC2018/0103 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley" @default.
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- W2890939466 date "2018-07-22" @default.
- W2890939466 modified "2023-09-24" @default.
- W2890939466 title "A Deep Residual Network with Transfer Learning for Pixel-level Road Crack Detection" @default.
- W2890939466 doi "https://doi.org/10.22260/isarc2018/0103" @default.
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