{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T03:36:47Z","timestamp":1775187407421,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funding for the Central Universities of Ministry of Education of China","award":["18D110408"],"award-info":[{"award-number":["18D110408"]}]},{"name":"Fundamental Research Funding for the Central Universities of Ministry of Education of China","award":["JMRH-2018-1042"],"award-info":[{"award-number":["JMRH-2018-1042"]}]},{"name":"Fundamental Research Funding for the Central Universities of Ministry of Education of China","award":["18K10454"],"award-info":[{"award-number":["18K10454"]}]},{"name":"Special Project Funding for the Shanghai Municipal Commission of Economy and Information Civil-Military Inosculation Project \u201cBig Data Management System of UAVs\u201d","award":["18D110408"],"award-info":[{"award-number":["18D110408"]}]},{"name":"Special Project Funding for the Shanghai Municipal Commission of Economy and Information Civil-Military Inosculation Project \u201cBig Data Management System of UAVs\u201d","award":["JMRH-2018-1042"],"award-info":[{"award-number":["JMRH-2018-1042"]}]},{"name":"Special Project Funding for the Shanghai Municipal Commission of Economy and Information Civil-Military Inosculation Project \u201cBig Data Management System of UAVs\u201d","award":["18K10454"],"award-info":[{"award-number":["18K10454"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["18D110408"],"award-info":[{"award-number":["18D110408"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["JMRH-2018-1042"],"award-info":[{"award-number":["JMRH-2018-1042"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["18K10454"],"award-info":[{"award-number":["18K10454"]}]},{"name":"Fundamental Research Funding for the Central Universities of Ministry of Education of China","award":["18D110408"],"award-info":[{"award-number":["18D110408"]}]},{"name":"Fundamental Research Funding for the Central Universities of Ministry of Education of China","award":["JMRH-2018-1042"],"award-info":[{"award-number":["JMRH-2018-1042"]}]},{"name":"Fundamental Research Funding for the Central Universities of Ministry of Education of China","award":["18K10454"],"award-info":[{"award-number":["18K10454"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>An improved maritime object detection algorithm, SRC-YOLO, based on the YOLOv4-tiny, is proposed in the foggy environment to address the issues of false detection, missed detection, and low detection accuracy in complicated situations. To confirm the model\u2019s validity, an ocean dataset containing various concentrations of haze, target angles, and sizes was produced for the research. Firstly, the Single Scale Retinex (SSR) algorithm was applied to preprocess the dataset to reduce the interference of the complex scenes on the ocean. Secondly, in order to increase the model\u2019s receptive field, we employed a modified Receptive Field Block (RFB) module in place of the standard convolution in the Neck part of the model. Finally, the Convolutional Block Attention Module (CBAM), which integrates channel and spatial information, was introduced to raise detection performance by expanding the network model\u2019s attention to the context information in the feature map and the object location points. The experimental results demonstrate that the improved SRC-YOLO model effectively detects marine targets in foggy scenes by increasing the mean Average Precision (mAP) of detection results from 79.56% to 86.15%.<\/jats:p>","DOI":"10.3390\/s22207786","type":"journal-article","created":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T01:44:13Z","timestamp":1665711853000},"page":"7786","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Research of Maritime Object Detection Method in Foggy Environment Based on Improved Model SRC-YOLO"],"prefix":"10.3390","volume":"22","author":[{"given":"Yihong","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Donghua University, Shanghai 201620, China"}]},{"given":"Hang","family":"Ge","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Donghua University, Shanghai 201620, China"}]},{"given":"Qin","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Donghua University, Shanghai 201620, China"}]},{"given":"Ming","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Donghua University, Shanghai 201620, China"}]},{"given":"Qiantao","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Donghua University, Shanghai 201620, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mou, X., Chen, X., Guan, J., Chen, B., and Dong, Y. 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