{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:01:24Z","timestamp":1770832884300,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T00:00:00Z","timestamp":1677024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Council of Canada"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Visual inspection of concrete structures using Unmanned Areal Vehicle (UAV) imagery is a challenging task due to the variability of defects\u2019 size and appearance. This paper proposes a high-performance model for automatic and fast detection of bridge concrete defects using UAV-acquired images. Our method, coined the Saliency-based Multi-label Defect Detector (SMDD-Net), combines pyramidal feature extraction and attention through a one-stage concrete defect detection model. The attention module extracts local and global saliency features, which are scaled and integrated with the pyramidal feature extraction module of the network using the max-pooling, multiplication, and residual skip connections operations. This has the effect of enhancing the localisation of small and low-contrast defects, as well as the overall accuracy of detection in varying image acquisition ranges. Finally, a multi-label loss function detection is used to identify and localise overlapping defects. The experimental results on a standard dataset and real-world images demonstrated the performance of SMDD-Net with regard to state-of-the-art techniques. The accuracy and computational efficiency of SMDD-Net make it a suitable method for UAV-based bridge structure inspection.<\/jats:p>","DOI":"10.3390\/rs15051218","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T01:31:06Z","timestamp":1677115866000},"page":"1218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Leveraging Saliency in Single-Stage Multi-Label Concrete Defect Detection Using Unmanned Aerial Vehicle Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9891-506X","authenticated-orcid":false,"given":"Loucif","family":"Hebbache","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of Quebec in Outaouais, Gatineau, QC J8X 3X7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5653-9644","authenticated-orcid":false,"given":"Dariush","family":"Amirkhani","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Quebec in Outaouais, Gatineau, QC J8X 3X7, Canada"}]},{"given":"Mohand Sa\u00efd","family":"Allili","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Quebec in Outaouais, Gatineau, QC J8X 3X7, Canada"}]},{"given":"Nadir","family":"Hammouche","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Quebec in Outaouais, Gatineau, QC J8X 3X7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2373-8035","authenticated-orcid":false,"given":"Jean-Fran\u00e7ois","family":"Lapointe","sequence":"additional","affiliation":[{"name":"Digital Technologies Research Center, National Research Council Canada, Ottawa, ON K1A 0R6, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1080\/10168664.2018.1558033","article-title":"Once upon a time in Italy: The tale of the Morandi Bridge","volume":"29","author":"Calvi","year":"2019","journal-title":"Struct. 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