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Pixel-level road crack detection in UAV remote sensing images based on ARD-Unet

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成果类型:
期刊论文
作者:
Gao, Yuxi;Cao, Hongbin;Cai, Weiwei;Zhou, Guoxiong
通讯作者:
Zhou, GX
作者机构:
[Gao, Yuxi; Zhou, Guoxiong; Cao, Hongbin] Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Peoples R China.
[Cai, Weiwei] Jiangnan Univ, Sch Artificial Intelligence & Comp Sci, Wuxi 214122, Peoples R China.
通讯机构:
[Zhou, GX ] C
Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Peoples R China.
语种:
英文
关键词:
Aircraft detection;Crack detection;Deterioration;Motor transportation;Remote sensing;Roads and streets;Unmanned aerial vehicles (UAV);Image-based;Multi-scale features;Pixel level;Pixel-by-pixel detection;Remote sensing images;Road cracks;Road damage;Road safety;Road traffic;UAV remote sensing;Pixels
期刊:
Measurement
ISSN:
0263-2241
年:
2023
卷:
219
页码:
113252
基金类别:
This work was supported by the National Natural Science Foundation of China (Grant No. 61703441), the Changsha Municipal Natural Science Foundation (Grant No. kq2014160), the key projects of the Department of Education of Hunan Province (Grant No. 19A511), and the Hunan Key Laboratory of Intelligent Logistics Technology (2019TP1015).
机构署名:
本校为第一且通讯机构
院系归属:
计算机与信息工程学院
摘要:
Crack is the main manifestation of road damage, and its further deterioration will affect road traffic. The timely detection of road cracks is of great significance for ensuring road safety. In this work, starting from UAV remote sensing images,a pixel-by-pixel crack detection method named ARD-Unet is proposed based on U-Net combined with Depth Separable Residual Block (DR-Block), Atrous Spatial Pyramid Fusion Attention Module (ASAM) and Receptive Field Block (RFB). We used UAV to construct a remote sensing road crack dataset containing 1046 hi...

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