{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T20:32:03Z","timestamp":1776371523941,"version":"3.51.2"},"reference-count":55,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T00:00:00Z","timestamp":1664496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NSERC Discovery Grant program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Conventional practices of bridge visual inspection present several limitations, including a tedious process of analyzing images manually to identify potential damages. Vision-based techniques, particularly Deep Convolutional Neural Networks, have been widely investigated to automatically identify, localize, and quantify defects in bridge images. However, massive datasets with different annotation levels are required to train these deep models. This paper presents a dataset of more than 6900 images featuring three common defects of concrete bridges (i.e., cracks, efflorescence, and spalling). To overcome the challenge of limited training samples, three Transfer Learning approaches in fine-tuning the state-of-the-art Visual Geometry Group network were studied and compared to classify the three defects. The best-proposed approach achieved a high testing accuracy (97.13%), combined with high F1-scores of 97.38%, 95.01%, and 97.35% for cracks, efflorescence, and spalling, respectively. Furthermore, the effectiveness of interpretable networks was explored in the context of weakly supervised semantic segmentation using image-level annotations. Two gradient-based backpropagation interpretation techniques were used to generate pixel-level heatmaps and localize defects in test images. Qualitative results showcase the potential use of interpretation maps to provide relevant information on defect localization in a weak supervision framework.<\/jats:p>","DOI":"10.3390\/rs14194882","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"4882","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Concrete Bridge Defects Identification and Localization Based on Classification Deep Convolutional Neural Networks and Transfer Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Hajar","family":"Zoubir","sequence":"first","affiliation":[{"name":"Laboratory of Systems Engeneering (LaGes), Hassania School of Public Works, Casablanca 20000, Morocco"}]},{"given":"Mustapha","family":"Rguig","sequence":"additional","affiliation":[{"name":"Laboratory of Systems Engeneering (LaGes), Hassania School of Public Works, Casablanca 20000, Morocco"}]},{"given":"Mohamed","family":"El Aroussi","sequence":"additional","affiliation":[{"name":"Laboratory of Systems Engeneering (LaGes), Hassania School of Public Works, Casablanca 20000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4193-6062","authenticated-orcid":false,"given":"Abdellah","family":"Chehri","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0197-8313","authenticated-orcid":false,"given":"Rachid","family":"Saadane","sequence":"additional","affiliation":[{"name":"Laboratory of Systems Engeneering (LaGes), Hassania School of Public Works, Casablanca 20000, Morocco"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0651-4278","authenticated-orcid":false,"given":"Gwanggil","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"04020117","DOI":"10.1061\/(ASCE)ST.1943-541X.0002666","article-title":"Probabilistic Life Prediction for Reinforced Concrete Structures Subjected to Seasonal Corrosion-Fatigue Damage","volume":"146","author":"Ma","year":"2020","journal-title":"J. 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