Fast Classification and Detection of Marine Targets in Complex Scenes with YOLOv3
Tingchao Shi, Mingyong Liu, Yang Yang, Sainan Li, Peixin Wang, Yuxuan Huang · OCEANS 2019 - Marseille · 2019
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.