{"title":"基于改进型 YOLOv8 的雾天近海船舶探测技术","authors":"Shirui Liang, Xiuwen Liu, Zaifei Yang, Mingchen Liu, Yong Yin","doi":"10.3390/jmse12091641","DOIUrl":null,"url":null,"abstract":"The detection and surveillance of ship targets in coastal waters is not only a crucial technology for the advancement of ship intelligence, but also holds great significance for the safety and economic development of coastal areas. However, due to poor visibility in foggy conditions, the effectiveness of ship detection in coastal waters during foggy weather is limited. In this paper, we propose an improved version of YOLOv8s, termed YOLOv8s-Fog, which provides a multi-target detection network specifically designed for nearshore scenes in foggy weather. This improvement involves adding coordinate attention to the neck of YOLOv8 and replacing the convolution in C2f with deformable convolution. Additionally, to expand the dataset, we construct and synthesize a collection of ship target images captured in coastal waters on days with varying degrees of fog, using the atmospheric scattering model and monocular depth estimation. We compare the improved model with the standard YOLOv8s model, as well as several other object detection models. The results demonstrate superior performance achieved by the improved model, achieving an average accuracy of 74.4% (mAP@0.5), which is 1.2% higher than that achieved by the standard YOLOv8s model.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Offshore Ship Detection in Foggy Weather Based on Improved YOLOv8\",\"authors\":\"Shirui Liang, Xiuwen Liu, Zaifei Yang, Mingchen Liu, Yong Yin\",\"doi\":\"10.3390/jmse12091641\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The detection and surveillance of ship targets in coastal waters is not only a crucial technology for the advancement of ship intelligence, but also holds great significance for the safety and economic development of coastal areas. However, due to poor visibility in foggy conditions, the effectiveness of ship detection in coastal waters during foggy weather is limited. In this paper, we propose an improved version of YOLOv8s, termed YOLOv8s-Fog, which provides a multi-target detection network specifically designed for nearshore scenes in foggy weather. This improvement involves adding coordinate attention to the neck of YOLOv8 and replacing the convolution in C2f with deformable convolution. Additionally, to expand the dataset, we construct and synthesize a collection of ship target images captured in coastal waters on days with varying degrees of fog, using the atmospheric scattering model and monocular depth estimation. We compare the improved model with the standard YOLOv8s model, as well as several other object detection models. The results demonstrate superior performance achieved by the improved model, achieving an average accuracy of 74.4% (mAP@0.5), which is 1.2% higher than that achieved by the standard YOLOv8s model.\",\"PeriodicalId\":16168,\"journal\":{\"name\":\"Journal of Marine Science and Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Marine Science and Engineering\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.3390/jmse12091641\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MARINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Marine Science and Engineering","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.3390/jmse12091641","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MARINE","Score":null,"Total":0}
Offshore Ship Detection in Foggy Weather Based on Improved YOLOv8
The detection and surveillance of ship targets in coastal waters is not only a crucial technology for the advancement of ship intelligence, but also holds great significance for the safety and economic development of coastal areas. However, due to poor visibility in foggy conditions, the effectiveness of ship detection in coastal waters during foggy weather is limited. In this paper, we propose an improved version of YOLOv8s, termed YOLOv8s-Fog, which provides a multi-target detection network specifically designed for nearshore scenes in foggy weather. This improvement involves adding coordinate attention to the neck of YOLOv8 and replacing the convolution in C2f with deformable convolution. Additionally, to expand the dataset, we construct and synthesize a collection of ship target images captured in coastal waters on days with varying degrees of fog, using the atmospheric scattering model and monocular depth estimation. We compare the improved model with the standard YOLOv8s model, as well as several other object detection models. The results demonstrate superior performance achieved by the improved model, achieving an average accuracy of 74.4% (mAP@0.5), which is 1.2% higher than that achieved by the standard YOLOv8s model.
期刊介绍:
Journal of Marine Science and Engineering (JMSE; ISSN 2077-1312) is an international, peer-reviewed open access journal which provides an advanced forum for studies related to marine science and engineering. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.