Pavement Defect Detection with Deep Learning: A Comprehensive Survey
Lili Fan1,2; Dandan Wang3; Junhao Wang3; Yunjie Li4; Yifeng Cao5; Yi Liu6; Xiaoming Chen; Yutong Wang
Source PublicationIEEE Transactions on Intelligent Vehicles
ISSN2379-8858
2023-10-19
Volume9Issue:3Pages:4292 - 4311
Corresponding AuthorWang, Yutong(yutong.wang@ia.ac.cn)
Abstract

Pavement defect detection is of profound significance regarding road safety, so it has been a trending research topic. In the past years, deep learning based methods have turned into a key technology, with advantages of high accuracy, strong robustness, and adaptability to complex pavement environments. This paper first reviews the methods based on image processing and 3D imaging. As for image-based processing techniques, they can be classified into three types based on how to label the collected data: fully supervised learning, unsupervised learning, and other methods. Different methods are further classified and compared with each other. Second, the pavement detection methods based on 3D data are sorted out, thereby summarizing their benefits, drawbacks, and application scenarios. Third, the study proposed the major challenges in the field of pavement defect detection, introduced validated datasets and evaluation metrics. Finally, on the basis of reviewing the literature in pavement defect detection, the promising direction is put forward.

KeywordDeep learning pavement defect detection computer vision image processing 3D image
DOI10.1109/TIV.2023.3326136
WOS Keyword3D ASPHALT SURFACES ; CRACK DETECTION ; CLASSIFICATION ; NETWORK ; SEGMENTATION ; EXTRACTION
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China
Funding OrganizationNational Key Research and Development Program of China
WOS Research AreaComputer Science ; Engineering ; Transportation
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:001214544700017
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sub direction classification目标检测、跟踪与识别
planning direction of the national heavy laboratory视觉信息处理
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57292
Collection多模态人工智能系统全国重点实验室_平行智能技术与系统团队
Corresponding AuthorYutong Wang
Affiliation1.the School of Information and Electronics, Beijing Institute of Technology
2.the Laboratory of Electromagnetic Space Cognition and Intelligent Control
3.the School of Science, Dalian Minzu University
4.the School of Information and Electronics, Beijing Institute of Technology
5.the Department of Mechanical and Mechatronics Engineering
6.the Jiangsu Industrial Innovation Center of Intelligent Equipment Company Ltd
7.the College of Mechanical and Vehicle Engineering, Hunan University
8.the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Lili Fan,Dandan Wang,Junhao Wang,et al. Pavement Defect Detection with Deep Learning: A Comprehensive Survey[J]. IEEE Transactions on Intelligent Vehicles,2023,9(3):4292 - 4311.
APA Lili Fan.,Dandan Wang.,Junhao Wang.,Yunjie Li.,Yifeng Cao.,...&Yutong Wang.(2023).Pavement Defect Detection with Deep Learning: A Comprehensive Survey.IEEE Transactions on Intelligent Vehicles,9(3),4292 - 4311.
MLA Lili Fan,et al."Pavement Defect Detection with Deep Learning: A Comprehensive Survey".IEEE Transactions on Intelligent Vehicles 9.3(2023):4292 - 4311.
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