Fast Classification and Detection of Marine Targets in Complex Scenes with YOLOv3

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.