文献类型：期刊文献

英文题名：Preliminary Investigation on Marine Radar Oil Spill Monitoring Method Using YOLO Model

作者：Li, Bo[1,2];Xu, Jin[1,2];Pan, Xinxiang[1,2];Chen, Rong[1,2];Ma, Long[1,2];Yin, Jianchuan[1];Liao, Zhiqiang[1];Chu, Lilin[1];Zhao, Zhiqiang[1,2];Lian, Jingjing[3];Wang, Haixia[3]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang 524091, Peoples R China;[2]Guangdong Ocean Univ, Shenzhen Inst, Shenzhen 518116, Peoples R China;[3]Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China

年份：2023

卷号：11

期号：3

外文期刊名：JOURNAL OF MARINE SCIENCE AND ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:000958601000001)、、Scopus(收录号：2-s2.0-85151396643)、WOS

基金：This research was funded by the National Natural Science Foundation of China, grant numbers 52071090, 51879024; the Natural Science Foundation of Guangdong Province, grant number 2022A1515011603; the University Special Projects of Guangdong Province, grant number 2022ZDZX3005; the Natural Science Foundation of Shenzhen, grant number JCYJ20220530162200001; and the Research start-up funding project of Guangdong Ocean University, grant numbers 060302132009, 060302132106.

语种：英文

外文关键词：marine radar; oil spill; YOLO

外文摘要：Due to the recent rapid growth of ocean oil development and transportation, the offshore oil spill risk accident probability has increased unevenly. The marine oil spill poses a great threat to the development of coastal cities. Therefore, effective and reliable technologies must be used to monitor oil spills to minimize disaster losses. Based on YOLO deep learning network, an automatic oil spill detection method was proposed. The experimental data preprocessing operations include noise reduction, gray adjustment, and local contrast enhancement. Then, real and synthetically generated marine radar oil spill images were used to make slice samples for training the model in the YOLOv5 network. The detection model can identify the effective oil spill monitoring region. Finally, an adaptive threshold was applied to extract the oil slicks in the effective oil spill monitoring regions. The YOLOv5 detection model generated had the advantage of high efficiency compared with existing methods. The offshore oil spill detection method proposed can support real-time and effective data for routine patrol inspection and accident emergency response.