eprintid: 10192868
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/19/28/68
datestamp: 2024-05-30 13:06:54
lastmod: 2024-05-30 13:06:54
status_changed: 2024-05-30 13:06:54
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Chu, H
creators_name: Chen, W
creators_name: Deng, L
title: Cascade operation-enhanced high-resolution representation learning for meticulous segmentation of bridge cracks
ispublished: pub
divisions: UCL
divisions: B04
divisions: C04
keywords: Deep learning, Crack segmentation, Cascade operation, Attention mechanism, High-resolution crack images, UAV crack inspection
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: High-resolution (HR) crack images have proven valuable for bridge inspection using unmanned aerial vehicles (UAVs), offering fine details crucial for accurate segmentation. Traditional deep learning (DL) struggles with HR images due to downsampling issues and limited computational resources. To address this, we propose Cascade-FcaHRNet, a HR representation learning-based multiscale architecture. It incorporates a frequency-channel attention mechanism to capture tiny crack features, a two-stage cascade operation for global and local refinement, and a region-sensitive loss to avoid ambiguous predictions. Ablation studies confirm the effectiveness of these modifications. Robustness experiments show improvements in performance metrics for crack segmentation. In a field test, the Cascade-FcaHRNet accurately segments bridge cracks wider than 0.5 mm from 4 K resolution images, enhancing safety and efficiency in UAV-based bridge inspection. The approach holds potential for developing scientifically sound maintenance and management strategies.
date: 2024-08-01
date_type: published
publisher: Elsevier BV
official_url: http://dx.doi.org/10.1016/j.aei.2024.102508
full_text_type: other
language: eng
verified: verified_manual
elements_id: 2266394
doi: 10.1016/j.aei.2024.102508
lyricists_name: Chen, Weiwei
lyricists_id: WCHEF68
actors_name: Chen, Weiwei
actors_id: WCHEF68
actors_role: owner
full_text_status: restricted
publication: Advanced Engineering Informatics
volume: 61
article_number: 102508
issn: 1474-0346
citation:        Chu, H;    Chen, W;    Deng, L;      (2024)    Cascade operation-enhanced high-resolution representation learning for meticulous segmentation of bridge cracks.                   Advanced Engineering Informatics , 61     , Article 102508.  10.1016/j.aei.2024.102508 <https://doi.org/10.1016/j.aei.2024.102508>.      
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10192868/1/Cascade%20Operation-Enhanced%20High-Resolution%20Representation%20Learning%20for%20Meticulous%20Segmentation%20of%20Bridge%20Cracks-%20accepted%20version.pdf