{"title":"Fast Classification and Detection of Marine Targets in Complex Scenes with YOLOv3","authors":"Tingchao Shi, Mingyong Liu, Yang Yang, Sainan Li, Peixin Wang, Yuxuan Huang","doi":"10.1109/OCEANSE.2019.8867137","DOIUrl":null,"url":null,"abstract":"In order to meet the needs of fast detection and classification of different marine targets during intelligent unmanned surface vehicle (USV) operations, In this paper, I introduce a convolutional neural network based on one of the most effective object detection algorithms, named YOLOv3, to classify and detect images of different marine targets. Firstly, I showed the network structure of the algorithm in this paper. Then, I explained how I got the optimal anchor box parameter of the algorithm. Finally, I improved the activation function to make the algorithm more robust to noise. The final results show that the MAP of the detector in this paper is 91.83%,and we reach a detection rate of 58.3 fps by improving the YOLOV3 algorithm.","PeriodicalId":375793,"journal":{"name":"OCEANS 2019 - Marseille","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"OCEANS 2019 - Marseille","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OCEANSE.2019.8867137","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In order to meet the needs of fast detection and classification of different marine targets during intelligent unmanned surface vehicle (USV) operations, In this paper, I introduce a convolutional neural network based on one of the most effective object detection algorithms, named YOLOv3, to classify and detect images of different marine targets. Firstly, I showed the network structure of the algorithm in this paper. Then, I explained how I got the optimal anchor box parameter of the algorithm. Finally, I improved the activation function to make the algorithm more robust to noise. The final results show that the MAP of the detector in this paper is 91.83%,and we reach a detection rate of 58.3 fps by improving the YOLOV3 algorithm.