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Sample and Structure-Guided Network for Road Crack Detection
Wu, Siyuan1,2; Fang, Jie1; Zheng, Xiangtao1
作者部门光谱成像技术研究室
2019
发表期刊IEEE ACCESS
ISSN2169-3536
卷号7页码:130032-130043
产权排序1
摘要

As an indispensable task for traffic management department, road maintenance has attracted much attention during the last decade due to the rapid development of traffic network. As is known, crack is the early form of many road damages, and repair it in time can significantly save the maintenance cost. In this case, how to detect crack regions quickly and accurately becomes a huge demand. Actually, many image processing technique based methods have been proposed for crack detection, but their performances can not meet our expectations. The reason is that, most of these methods use bottom features such as color and texture to detect the cracks, which are easily influenced by the varied conditions such as light and shadow. Inspired by the great successes of machine learning and artificial intelligence, this paper presents a sample and structure guided network for detecting road cracks. Specifically, the proposed network is based on U-Net architecture, which remains the details from input to output by using skip connection strategy. Then, because the scale of crack samples is much smaller than that of non-crack ones, directly using the conventional cross entropy loss can not optimize the network effectively. In this case, the Focal loss is utilized to address the model optimization problem. Additionally, we incorporate the self-attention strategy into the proposed network, which enhances its stability by encoding the 2-order information among different local regions into the final features. Finally, we test the proposed method on four datasets, three public ones with labels and a photographed one without labels, to validate its effectiveness. It is noteworthy that, for the photographed dataset, we design a series of image processing strategies such as contrast enhancement to improve the generalization capability of the proposed method.

关键词Road crack detection neural network representation capability sample imbalance structural information
DOI10.1109/ACCESS.2019.2940767
收录类别SCI ; EI
语种英语
WOS记录号WOS:000487541200006
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI入藏号20200308030780
引用统计
被引频次:29[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/31883
专题光谱成像技术研究室
通讯作者Zheng, Xiangtao
作者单位1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol CAS, Xian 710119, Shaanxi, Peoples R China.
2.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714
Wu, Siyuan,Fang, Jie,Zheng, Xiangtao. Sample and Structure-Guided Network for Road Crack Detection[J]. IEEE ACCESS,2019,7:130032-130043.
APA Wu, Siyuan,Fang, Jie,&Zheng, Xiangtao.(2019).Sample and Structure-Guided Network for Road Crack Detection.IEEE ACCESS,7,130032-130043.
MLA Wu, Siyuan,et al."Sample and Structure-Guided Network for Road Crack Detection".IEEE ACCESS 7(2019):130032-130043.
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