Shan, J., Jiang, W., Huang, Y. orcid.org/0000-0002-1220-6896 et al. (2 more authors) (2024) Unmanned Aerial Vehicle (UAV)-Based Pavement Image Stitching Without Occlusion, Crack Semantic Segmentation, and Quantification. IEEE Transactions on Intelligent Transportation Systems, 25 (11). pp. 17038-17053. ISSN 1524-9050
Abstract
Unmanned Aerial Vehicle (UAV)-based pavement distress detection offers efficient and safe advantages. However, obstructions from road vehicles and the slender shape of cracks in UAV images challenge accuracy. To address this, this study established specific flight parameters, proposed the Historical Best Matching Image (HBMI) approach for data loss due to obstructions, and created the UAV-Crack500 dataset with 500 finely annotated crack images. Three algorithms with different loss functions were investigated, finding that the U-Net network combined with our Completely Asymmetric Loss (CAL) achieved the best performance, resolving the issue of class imbalance. Morphological analysis of the semantically segmented images provided precise crack morphology features. In complex scenarios, errors in features like crack area, length, mean width, and maximum width remained within 16%. This study establishes a comprehensive UAV-based pavement distress detection system, overcoming obstructions for accurate assessment.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Pavement distress, semantic segmentation, crack quantification, image stitching, unmanned aerial vehicle (UAV) |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Nov 2024 12:36 |
Last Modified: | 29 Nov 2024 14:29 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/tits.2024.3424525 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220239 |