基于YOLOv3的复杂场景下海洋目标快速分类与检测

Tingchao Shi, Mingyong Liu, Yang Yang, Sainan Li, Peixin Wang, Yuxuan Huang
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引用次数: 4

摘要

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。
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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.
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