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Intell."],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:p> In the context of real-world environments, images acquired through surveillance cameras in such settings are frequently marred by issues including diminished contrast, suboptimal image quality, and color aberrations, rendering conventional object detection models ill-suited for the task. Taking inspiration from the foundational principles of image restoration, this study aims to extract environment-agnostic features across various weather conditions in order to enhance object detection performance in multiple scenarios while maintaining accuracy under typical meteorological conditions. In response to this question, we introduce a detection framework as HDR-YOLO that jointly trains feature extraction and object detection. Meantime, to solve the problem of visual impairments caused by adverse conditions, we propose a Dynamic Extraction of Environment-Agnostic Features (DEAF) module. Additionally, we joint mean squared error (MSE) loss and Log-Cosh loss as optimization techniques, carefully tailored to further elevate detection performance, especially under adverse meteorological conditions. Extensive empirical findings from the AGVS dataset validate the ability of HDR-YOLO to improve object detection performance in airport ground videos within real-world settings while maintaining precision under typical meteorological conditions, which underscores its innovative capabilities and adaptability in complex and diverse environments. <\/jats:p>","DOI":"10.1142\/s021800142450006x","type":"journal-article","created":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T12:01:52Z","timestamp":1712836912000},"source":"Crossref","is-referenced-by-count":3,"title":["HDR-YOLO: Adaptive Object Detection in Haze, Dark, and Rain Scenes Based on YOLO"],"prefix":"10.1142","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9427-1423","authenticated-orcid":false,"given":"Zonglei","family":"Lyu","sequence":"first","affiliation":[{"name":"Laboratory of Smart Airport Theory and System of CAAC, Civil Aviation University of China, Tianjin, China"}]},{"given":"Wei","family":"An","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Civil Aviation University of China, Tianjin, China"}]}],"member":"219","published-online":{"date-parts":[[2024,5,31]]},"reference":[{"key":"S021800142450006XBIB001","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12806"},{"key":"S021800142450006XBIB002","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01170"},{"key":"S021800142450006XBIB003","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00934"},{"key":"S021800142450006XBIB004","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01447-x"},{"key":"S021800142450006XBIB005","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"S021800142450006XBIB006","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"S021800142450006XBIB007","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3336952"},{"key":"S021800142450006XBIB008","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"S021800142450006XBIB009","doi-asserted-by":"publisher","DOI":"10.1109\/TTE.2021.3080690"},{"key":"S021800142450006XBIB010","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.168"},{"key":"S021800142450006XBIB011","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP42928.2021.9506039"},{"key":"S021800142450006XBIB012","doi-asserted-by":"publisher","DOI":"10.1109\/ICCE-Taiwan49838.2020.9258127"},{"key":"S021800142450006XBIB013","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01240-3_44"},{"key":"S021800142450006XBIB014","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3125679"},{"issue":"8","key":"S021800142450006XBIB015","first-page":"2623","volume":"43","author":"Huang S. 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