{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T05:31:56Z","timestamp":1769059916862,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T00:00:00Z","timestamp":1643068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Plan of Zhejiang Province, China","award":["2019C02050, 2020C03012, 2021C03181"],"award-info":[{"award-number":["2019C02050, 2020C03012, 2021C03181"]}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["31801619"],"award-info":[{"award-number":["31801619"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Vital transportation of hazardous and noxious substances (HNSs) by sea occasionally suffers spill incidents causing perilous mutilations to off-shore and on-shore ecology. Consequently, it is essential to monitor the spilled HNSs rapidly and mitigate the damages in time. Focusing on on-site and early processing, this paper explores the potential of deep learning and single-spectrum ultraviolet imaging (UV) for detecting HNSs spills. Images of three floating HNSs, including benzene, xylene, and palm oil, captured in different natural and artificial aquatic sites were collected. The image dataset involved UV (at 365 nm) and RGB images for training and comparative analysis of the detection system. The You Only Look Once (YOLOv3) deep learning model is modified to balance the higher accuracy and swift detection. With the MobileNetv2 backbone architecture and generalized intersection over union (GIoU) loss function, the model achieved mean IoU values of 86.57% for UV and 82.43% for RGB images. The model yielded a mean average precision (mAP) of 86.89% and 72.40% for UV and RGB images, respectively. The average speed of 57 frames per second (fps) and average detection time of 0.0119 s per image validated the swift performance of the proposed model. The modified deep learning model combined with UV imaging is considered computationally cost-effective resulting in precise detection accuracy and significantly faster detection speed.<\/jats:p>","DOI":"10.3390\/rs14030576","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T21:07:11Z","timestamp":1643144831000},"page":"576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Combining Deep Learning with Single-Spectrum UV Imaging for Rapid Detection of HNSs Spills"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7727-1859","authenticated-orcid":false,"given":"Syed Raza","family":"Mehdi","sequence":"first","affiliation":[{"name":"Department of Marine Engineering and Technology, Ocean College, Zhejiang University, Zhoushan 316021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazim","family":"Raza","sequence":"additional","affiliation":[{"name":"College of Optical Science and Engineering, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1204-3684","authenticated-orcid":false,"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Marine Engineering and Technology, Ocean College, Zhejiang University, Zhoushan 316021, China"},{"name":"Hainan Institute, Zhejiang University, Sanya 572025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7473-8441","authenticated-orcid":false,"given":"Rizwan Ali","family":"Naqvi","sequence":"additional","affiliation":[{"name":"Department of Unmanned Vehicle Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8083-5360","authenticated-orcid":false,"given":"Amjad","family":"Ali","sequence":"additional","affiliation":[{"name":"School of Electronics and Computer Engineering, Peking University, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1000-4764","authenticated-orcid":false,"given":"Hong","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Marine Engineering and Technology, Ocean College, Zhejiang University, Zhoushan 316021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.envint.2014.05.012","article-title":"Development of a risk-based prioritization methodology to inform public health emergency planning and preparedness in case of accidental spill at sea of hazardous and noxious substances (HNS)","volume":"72","author":"Harold","year":"2014","journal-title":"Environ. 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