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

作　　者：Lili Fan Shen Li Ying Li Bai Li Dongpu Cao Fei-Yue Wang 

机构地区：[1]School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China [2]School of Civil Engineering,Tsinghua University,Beijing 100084,China [3]School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China [4]State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body and the College of Mechanical and Vehicle Engineering,Hunan University,Changsha 410082,China [5]School of Vehicle and Mobility,Tsinghua University,Beijing 100084,China [6]State Key Laboratory for Management and Control of Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2023年第7期1593-1607,共15页自动化学报（英文版）

基　　金：supported in part by the 14th Five-Year Project of Ministry of Science and Technology of China(2021YFD2000304);Fundamental Research Funds for the Central Universities(531118010509);Natural Science Foundation of Hunan Province,China(2021JJ40114)。

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.

关 键 词：Automatic pavement crack detection data augmentation compensation deep learning residual feature augmentation shadow removal shadow-crack dataset 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] U418.66[自动化与计算机技术—计算机科学与技术]