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

Tingchao Shi, Mingyong Liu, Yang Yang, Sainan Li, Peixin Wang, Yuxuan Huang
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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.
基于YOLOv3的复杂场景下海洋目标快速分类与检测
为了满足智能无人水面车辆(USV)作战过程中对不同海洋目标的快速检测和分类需求,本文基于目前最有效的目标检测算法之一YOLOv3,引入卷积神经网络对不同海洋目标图像进行分类和检测。首先,本文给出了算法的网络结构。然后,我解释了如何得到算法的最优锚盒参数。最后,我改进了激活函数,使算法对噪声具有更强的鲁棒性。最终结果表明,本文探测器的MAP为91.83%,通过改进YOLOV3算法达到58.3 fps的检测率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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