{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T20:48:51Z","timestamp":1760647731221,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T00:00:00Z","timestamp":1670457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Khalid University","award":["372\/43","PNURSP2022R151","RSP2022R444"],"award-info":[{"award-number":["372\/43","PNURSP2022R151","RSP2022R444"]}]},{"DOI":"10.13039\/501100004242","name":"Princess Nourah bint Abdulrahman University","doi-asserted-by":"publisher","award":["372\/43","PNURSP2022R151","RSP2022R444"],"award-info":[{"award-number":["372\/43","PNURSP2022R151","RSP2022R444"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]},{"name":"King Saud University","award":["372\/43","PNURSP2022R151","RSP2022R444"],"award-info":[{"award-number":["372\/43","PNURSP2022R151","RSP2022R444"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Roads can be significant traffic lifelines that can be damaged by collapsed tree branches, landslide rubble, and buildings debris. Thus, road damage detection and evaluation by utilizing High-Resolution Remote Sensing Images (RSI) are highly important to maintain routes in optimal conditions and execute rescue operations. Detecting damaged road areas through high-resolution aerial images could promote faster and effectual disaster management and decision making. Several techniques for the prediction and detection of road damage caused by earthquakes are available. Recently, computer vision (CV) techniques have appeared as an optimal solution for road damage automated inspection. This article presents a new Road Damage Detection modality using the Hunger Games Search with Elman Neural Network (RDD\u2013HGSENN) on High-Resolution RSIs. The presented RDD\u2013HGSENN technique mainly aims to determine road damages using RSIs. In the presented RDD\u2013HGSENN technique, the RetinaNet model was applied for damage detection on a road. In addition, the RDD\u2013HGSENN technique can perform road damage classification using the ENN model. To tune the ENN parameters automatically, the HGS algorithm was exploited in this work. To examine the enhanced outcomes of the presented RDD\u2013HGSENN technique, a comprehensive set of simulations were conducted. The experimental outcomes demonstrated the improved performance of the RDD\u2013HGSENN technique with respect to recent approaches in relation to several measures.<\/jats:p>","DOI":"10.3390\/rs14246222","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T03:23:49Z","timestamp":1670556229000},"page":"6222","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Road Damage Detection Using the Hunger Games Search with Elman Neural Network on High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Mesfer","family":"Al Duhayyim","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7123-8548","authenticated-orcid":false,"given":"Areej A.","family":"Malibari","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}]},{"given":"Abdullah","family":"Alharbi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Community College, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi Arabia"}]},{"given":"Kallekh","family":"Afef","sequence":"additional","affiliation":[{"name":"Department of Mathematics, College of Science & Arts at Mahayil, King Khalid University, Mohail Asser, Abha 62521, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0320-2583","authenticated-orcid":false,"given":"Ayman","family":"Yafoz","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3163-575X","authenticated-orcid":false,"given":"Raed","family":"Alsini","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7378-5545","authenticated-orcid":false,"given":"Omar","family":"Alghushairy","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah 21589, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2003-0357","authenticated-orcid":false,"given":"Heba","family":"Mohsen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101182","DOI":"10.1016\/j.aei.2020.101182","article-title":"Survey on performance of deep learning models for detecting road damages using multiple dashcam image resources","volume":"46","author":"Cao","year":"2020","journal-title":"Adv. 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