由于受到水面的高反光性和波纹等边缘特征的影响,传统的水面目标识别算法不能很好地识别出目标。为此,提出基于深度学习的水面目标识别算法。首先采集大量的目标样本并对其进行标注,然后根据YOLOv3(You Only Look Once v3)算法的原理对算法的参数和网络结构进行优化,随后采用深度卷积神经网络的方法对目标样本进行训练。采用对目标样本进行数据增强的方式以适应不同环境进而提升算法的鲁棒性,采用相位相关性水岸线识别算法来提高识别速度。最后使用所提算法的网络结构训练所得的权重文件建立水面目标识别系统,该系统可以达到较高的识别率。实验结果验证所提算法的有效性和鲁棒性,对水面目标识别的后续研究有一定的参考价值。

Owing to the high reflectivity of the water surface and the influence of edge features such as ripples, the traditional water surface target recognition algorithm is unable to appropriately identify the target. To this end, a water surface target recognition algorithm based on deep learning is proposed herein. First, a large number of target samples are collected and labeled, then the parameters and network structure of the algorithm are optimized based on the principle of the YOLOv3 (You Only Look Once v3) algorithm. Then, the target samples are trained using the deep convolutional neural network. The data enhancement of the target sample is conducted to adapt to different environments to improve the robustness of the proposed algorithm, and the phase correlation waterfront recognition algorithm is used to improve the recognition speed. Finally, the weight file obtained from the network structure training of the proposed algorithm is used to establish a surface target recognition system, which can achieve a higher recognition rate. Experimental results verify the effectiveness and robustness of the proposed algorithm and can provide a reference for future research on surface target recognition.