{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T02:41:10Z","timestamp":1773369670345,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,25]],"date-time":"2023-02-25T00:00:00Z","timestamp":1677283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hubei Province unveiling project","award":["2022BEC024"],"award-info":[{"award-number":["2022BEC024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recent years have witnessed the increasing risk of subsea gas leaks with the development of offshore gas exploration, which poses a potential threat to human life, corporate assets, and the environment. The optical imaging-based monitoring approach has become widespread in the field of monitoring underwater gas leakage, but the shortcomings of huge labor costs and severe false alarms exist due to related operators\u2019 operation and judgment. This study aimed to develop an advanced computer vision-based monitoring approach to achieve automatic and real-time monitoring of underwater gas leaks. A comparison analysis between the Faster Region Convolutional Neural Network (Faster R-CNN) and You Only Look Once version 4 (YOLOv4) was conducted. The results demonstrated that the Faster R-CNN model, developed with an image size of 1280 \u00d7 720 and no noise, was optimal for the automatic and real-time monitoring of underwater gas leakage. This optimal model could accurately classify small and large-shape leakage gas plumes from real-world datasets, and locate the area of these underwater gas plumes.<\/jats:p>","DOI":"10.3390\/s23052566","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T02:10:46Z","timestamp":1677463846000},"page":"2566","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Advanced Computer Vision-Based Subsea Gas Leaks Monitoring: A Comparison of Two Approaches"],"prefix":"10.3390","volume":"23","author":[{"given":"Hongwei","family":"Zhu","sequence":"first","affiliation":[{"name":"Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6395-7075","authenticated-orcid":false,"given":"Weikang","family":"Xie","sequence":"additional","affiliation":[{"name":"Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7973-0310","authenticated-orcid":false,"given":"Junjie","family":"Li","sequence":"additional","affiliation":[{"name":"Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2639-8972","authenticated-orcid":false,"given":"Jihao","family":"Shi","sequence":"additional","affiliation":[{"name":"Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China"},{"name":"Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China"}]},{"given":"Mingfu","family":"Fu","sequence":"additional","affiliation":[{"name":"PipeChina West Pipeline Company, Urumqi 830000, China"},{"name":"School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}]},{"given":"Xiaoyuan","family":"Qian","sequence":"additional","affiliation":[{"name":"Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China"}]},{"given":"He","family":"Zhang","sequence":"additional","affiliation":[{"name":"Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Kaikai","family":"Wang","sequence":"additional","affiliation":[{"name":"Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Guoming","family":"Chen","sequence":"additional","affiliation":[{"name":"Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao 266580, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1002\/cjce.22345","article-title":"Current understanding of subsea gas release: A review","volume":"94","author":"Olsen","year":"2016","journal-title":"Can. 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