Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin

被引:7
|
作者
Wang, Weidong [1 ,2 ,3 ]
Xu, Xinyue [1 ,2 ,3 ]
Peng, Jun [1 ,2 ,3 ]
Hu, Wenbo [1 ,2 ,3 ]
Wu, Dingze [1 ,2 ,3 ]
机构
[1] Cent South Univ, Sch Civil Engn, Changsha 410075, Peoples R China
[2] Cent South Univ, MOE, Key Lab Engn Struct Heavy Haul Railway, Changsha 410075, Peoples R China
[3] Cent South Univ, Ctr Railway Infrastruct Smart Monitoring & Managem, Changsha 410075, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 07期
基金
中国国家自然科学基金;
关键词
road engineering; pavement-distress detection; digital twin; integrated data; physical engine; deep-object detection network; CONVOLUTIONAL NEURAL-NETWORKS; CRACK DETECTION; FRAMEWORK;
D O I
10.3390/app13074549
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The automated detection of distress such as cracks or potholes is a key basis for assessing the condition of pavements and deciding on their maintenance. A fine-grained pavement distress-detection algorithm based on integrated data using a digital twin is proposed to solve the challenges of the insufficiency of high-quality negative samples in specific scenarios An asphalt pavement background model is created based on UAV-captured images, and a lightweight physical engine is used to randomly render 5 types of distress and 3 specific scenarios to the background model, generating a digital twin model that can provide virtual distress data. The virtual data are combined with real data in different virtual-to-real ratios (0:1 to 5:1) to form an integrated dataset and used to fully train deep object detection networks for fine-grained detection. The results show that the YOLOv5 network with the virtual-to-real ratio of 3:1 achieves the best average precision for 5 types of distress (asphalt pavement MAP: 75.40%), with a 2-fold and 1.5-fold improvement compared to models developed without virtual data and with traditional data augmentation, respectively, and achieves over 40% recall in shadow, occlusion and blur. The proposed approach could provide a more reliable and refined automated method for pavement analysis in complex scenarios.
引用
收藏
页数:23
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