Pavement Cracks Coupled With Shadows: A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach

doi:10.1109/JAS.2023.123447

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	Pavement Cracks Coupled With Shadows: A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach
	Lili Fan; Shen Li; Ying Li; Bai Li; Dongpu Cao; Fei-Yue Wang
刊名	IEEE/CAA Journal of Automatica Sinica
	2023
卷号	10 期号:7 页码:1593-1607
关键词	Automatic pavement crack detection data augmentation compensation deep learning residual feature augmentation shadow removal shadow-crack dataset
ISSN号	2329-9266
DOI	10.1109/JAS.2023.123447
英文摘要	Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety. The task is challenging because the shadows on the pavement may have similar intensity with the crack, which interfere with the crack detection performance. Till to the present, there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows. To fill in the gap, we made several contributions as follows. First, we proposed a new pavement shadow and crack dataset, which contains a variety of shadow and pavement pixel size combinations. It also covers all common cracks (linear cracks and network cracks), placing higher demands on crack detection methods. Second, we designed a two-step shadow-removal-oriented crack detection approach: SROCD, which improves the performance of the algorithm by first removing the shadow and then detecting it. In addition to shadows, the method can cope with other noise disturbances. Third, we explored the mechanism of how shadows affect crack detection. Based on this mechanism, we propose a data augmentation method based on the difference in brightness values, which can adapt to brightness changes caused by seasonal and weather changes. Finally, we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters, and the algorithm improves the performance of the model overall. We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset, and the experimental results demonstrate the superiority of our method.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/51996]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Lili Fan,Shen Li,Ying Li,et al. Pavement Cracks Coupled With Shadows: A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(7):1593-1607.
APA	Lili Fan,Shen Li,Ying Li,Bai Li,Dongpu Cao,&Fei-Yue Wang.(2023).Pavement Cracks Coupled With Shadows: A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach.IEEE/CAA Journal of Automatica Sinica,10(7),1593-1607.
MLA	Lili Fan,et al."Pavement Cracks Coupled With Shadows: A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach".IEEE/CAA Journal of Automatica Sinica 10.7(2023):1593-1607.