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Self-adaptive 2D 3D image fusion for automated pixel-level pavement crack detection

Jiayv Jing, Xu Yang, Ling Ding, Hainian Wang, Jinchao Guan, Yue Hou Orcid Logo, Sherif M. El-Badawy

Automation in Construction, Volume: 168, Start page: 105756

Swansea University Author: Yue Hou Orcid Logo

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DOI (Published version): 10.1016/j.autcon.2024.105756

Abstract

Current 2D and 3D image-based crack detection methods in transportation infrastructure often struggle with noise robustness and feature diversity. To overcome these challenges, the paper use CSF-CrackNet, a self-adaptive 2Dsingle bond3D image fusion model utilizes channel and spatial modules for aut...

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Published in: Automation in Construction
ISSN: 0926-5805
Published: Elsevier BV 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa67682
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Abstract: Current 2D and 3D image-based crack detection methods in transportation infrastructure often struggle with noise robustness and feature diversity. To overcome these challenges, the paper use CSF-CrackNet, a self-adaptive 2Dsingle bond3D image fusion model utilizes channel and spatial modules for automated pavement crack segmentation. CSF-CrackNet consists of four parts: feature enhanced and field sensing (FEFS) module, channel module, spatial module, and semantic segmentation module. A multi-feature image dataset was established using a vehicle-mounted 3D imaging system, including color images, depth images, and color-depth overlapped images. Results show that the mean intersection over union (mIOU) of most models under the CSF-CrackNet framework can be increased to above 80 %. Compared with original RGB and depth images, the average mIOU increases with image fusion by 10 % and 5 %, respectively. The ablation experiment and weight significance analysis further demonstrate that CSF-CrackNet can significantly improve semantic segmentation performance by balancing information between 2D and 3D images.
Keywords: Pavement crack detection; Self-adaptive image fusion; Semantic segmentation; Multi-feature dataset
College: Faculty of Science and Engineering
Funders: This work was supported in part by the National Natural Science Foundation of China under Grant 52078049, Grant 52378431 and Grant 52408454, in part by the Fundamental Research Funds for the Central Universities, CHD under Grant 300102210302 and Grant 300102210118 and in part by the 111 Project of Low Carbon Smart Road Infrastructure Construction and Maintenance Discipline Innovation and Talent Introduction Base of Shaanxi Province.
Start Page: 105756

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