{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:47:25Z","timestamp":1774450045457,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T00:00:00Z","timestamp":1756252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation","award":["#2213694"],"award-info":[{"award-number":["#2213694"]}]},{"name":"National Science Foundation","award":["9-2244"],"award-info":[{"award-number":["9-2244"]}]},{"DOI":"10.13039\/100008882","name":"Texas State University","doi-asserted-by":"publisher","award":["#2213694"],"award-info":[{"award-number":["#2213694"]}],"id":[{"id":"10.13039\/100008882","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008882","name":"Texas State University","doi-asserted-by":"publisher","award":["9-2244"],"award-info":[{"award-number":["9-2244"]}],"id":[{"id":"10.13039\/100008882","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mathematics"],"abstract":"<jats:p>High-resolution 3D pavement images have become a valuable data source for automated surface distress detection and assessment. However, accurately identifying and segmenting cracks from pavement images remains challenging, due to factors such as low contrast and hair-like thinness. This study investigates key factors affecting segmentation performance and proposes a novel deep learning architecture designed to enhance segmentation robustness under these challenging conditions. The proposed model integrates a multi-resolution feature extraction stream with gated attention mechanisms to improve spatial awareness and selectively fuse information across feature levels. Our extensive experiments on a 3D pavement dataset demonstrated that the proposed method outperformed several state-of-the-art architectures, including FCN, U-Net, DeepLab, DeepCrack, and CrackFormer. Compared with U-Net, it improved F1 from 0.733 to 0.780. The gains were most pronounced on thin cracks, with F1 from 0.531 to 0.626. Our paired t-tests across folds showed the method is statistically better than U-Net and DeepCrack on Recall, IoU, Dice, and F1. These findings highlight the effectiveness of the attention-guided, multi-scale feature fusion method for robust crack segmentation using 3D pavement data.<\/jats:p>","DOI":"10.3390\/math13172752","type":"journal-article","created":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T08:19:32Z","timestamp":1756282772000},"page":"2752","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Multi-Resolution Attention U-Net for Pavement Distress Segmentation in 3D Images: Architecture and Data-Driven Insights"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5818-8411","authenticated-orcid":false,"given":"Haitao","family":"Gong","sequence":"first","affiliation":[{"name":"Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2051-5477","authenticated-orcid":false,"given":"Jueqiang","family":"Tao","sequence":"additional","affiliation":[{"name":"College of Engineering, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0686-450X","authenticated-orcid":false,"given":"Xiaohua","family":"Luo","sequence":"additional","affiliation":[{"name":"Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1528-9711","authenticated-orcid":false,"given":"Feng","family":"Wang","sequence":"additional","affiliation":[{"name":"Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1520\/JTE103331","article-title":"Automated measurements of road cracks using line-scan imaging","volume":"39","author":"Yao","year":"2011","journal-title":"J. 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