Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection
文献类型：期刊论文
| 作者 | Pang, Junbiao3; Xiong, Baocheng3; Wu, Jiaqi3; Huang, Qingming1,2 |
| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
 |
| 出版日期 | 2025-07-01 |
| 卷号 | 26期号:7页码:9165-9174 |
| 关键词 | Feature extraction Semantics Heating systems Context modeling Noise Convolution Asphalt Semantic segmentation YOLO Training Crack detection context information multi-scale spatial structure |
| ISSN号 | 1524-9050 |
| DOI | 10.1109/TITS.2024.3438883 |
| 英文摘要 | Pavement cracks have a highly complex spatialstructure, a low contrasting background and a weak spatialcontinuity, posing a significant challenge to an effective crackdetection method. To precisely localize crack from an image, it iscritical to effectively extract and aggregate multi-granularity con-text, including the fine-grained local context around the cracks(in spatial-level) and the coarse-grained semantics (in semantic-level). In this paper, we apply the dilated convolution as thebackbone feature extractor to model local context, then we builda context guidance module to leverage semantic context to guidelocal feature extraction at multiple stages. To handle label align-ment between stages, we apply the Multiple Instance Learning(MIL) strategy to align the feature between two stages. In addi-tion, to our best knowledge, we have released the largest, mostcomplex and most challenging Bitumen Pavement Crack (BPC)dataset. The experimental results on the three crack datasetsdemonstrate that the proposed method performs well and outper-forms the current state-of-the-art methods. On BPC, the proposedmodel achieved AP 88.32% with the 16.89 M parameters underthe 45.36 GFlops runing speed. Datset and code are publiclyavailable at: https://github.com/pangjunbiao/BPC-Crack-Dataset. |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:001508153100001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42369]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Pang, Junbiao |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China |
| 推荐引用方式 GB/T 7714 | Pang, Junbiao,Xiong, Baocheng,Wu, Jiaqi,et al. Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2025,26(7):9165-9174. |
| APA | Pang, Junbiao,Xiong, Baocheng,Wu, Jiaqi,&Huang, Qingming.(2025).Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,26(7),9165-9174. |
| MLA | Pang, Junbiao,et al."Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 26.7(2025):9165-9174. |
入库方式： OAI收割
来源：计算技术研究所
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