GGMNet: Pavement-Crack Detection Based on Global Context Awareness and Multi-Scale Fusion
文献类型：期刊论文
| 作者 | Wang, Yong4; He, Zhenglong3,4; Zeng, Xiangqiang3,4; Zeng, Juncheng2; Cen, Zongxi3,4; Qiu, Luyang1; Xu, Xiaowei2; Zhuo, Qunxiong1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-05-01 |
| 卷号 | 16期号:10页码:1797 |
| 关键词 | pavement cracks deep learning attention mechanism graph reasoning multi-scale features fusion |
| DOI | 10.3390/rs16101797 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Accurate and comprehensive detection of pavement cracks is important for maintaining road quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity of cracks make it difficult for existing methods to accomplish this challenging task. This paper proposes a novel network named the global graph multiscale network (GGMNet) for automated pixel-level detection of pavement cracks. The GGMNet network has several innovations compared with the mainstream road crack detection network: (1) a global contextual Res-block (GC-Resblock) is proposed to guide the network to emphasize the identities of cracks while suppressing background noises; (2) a graph pyramid pooling module (GPPM) is designed to aggregate the multi-scale features and capture the long-range dependencies of cracks; (3) a multi-scale features fusion module (MFF) is established to efficiently represent and deeply fuse multi-scale features. We carried out extensive experiments on three pavement crack datasets. These were DeepCrack dataset, with complex background noises; the CrackTree260 dataset, with various crack structures; and the Aerial Track Detection dataset, with a drone's perspective. The experimental results demonstrate that GGMNet has excellent performance, high accuracy, and strong robustness. In conclusion, this paper provides support for accurate and timely road maintenance and has important reference values and enlightening implications for further linear feature extraction research. |
| WOS关键词 | FEATURES ; NETWORK |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001231475300001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/205391]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Wang, Yong |
| 作者单位 | 1.Fujian Luoning Expressway Co Ltd, Fuzhou 350001, Peoples R China 2.Fujian Expressway Sci & Technol Innovat Res Inst C, Fuzhou 350001, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yong,He, Zhenglong,Zeng, Xiangqiang,et al. GGMNet: Pavement-Crack Detection Based on Global Context Awareness and Multi-Scale Fusion[J]. REMOTE SENSING,2024,16(10):1797. |
| APA | Wang, Yong.,He, Zhenglong.,Zeng, Xiangqiang.,Zeng, Juncheng.,Cen, Zongxi.,...&Zhuo, Qunxiong.(2024).GGMNet: Pavement-Crack Detection Based on Global Context Awareness and Multi-Scale Fusion.REMOTE SENSING,16(10),1797. |
| MLA | Wang, Yong,et al."GGMNet: Pavement-Crack Detection Based on Global Context Awareness and Multi-Scale Fusion".REMOTE SENSING 16.10(2024):1797. |
入库方式： OAI收割
来源：地理科学与资源研究所
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