基于卷积神经网络的水下目标特征提取方法

针对水下目标特征提取问题,在卷积神经网的基础上,提出了一种新的网络结构。该框架通过引入特征图多维加权层,强化了特征图的空间信息,弥补了进入全连接层时空间特征的丢失。以层次结构构造一个集特征提取和分类器训练于一体的端到端网络,同时利用深度网络的反向传播完成分类器对特征提取的反馈以改进特征的效果。在仿真模拟实验上,该网络框架分类目标达到了78.61%的精度,与其他方法相比,有效提高了目标的识别精度。所提框架能有效分类识别水下目标,具有良好的识别精度,且具备模块化结构,无需复杂预处理,实现简单。

Underwater target feature extraction method based on convolutional neural network

To solve the problem of underwater target feature extraction, a new network structure is proposed on the basis of convolution neural network. The framework enhances the spatial information of the feature map by introducing the feature graph multidimensional weighting layer, which makes up the loss of spatial features when entering the whole connection layer. This framework builds an end to end network using hierarchical structure for feature extraction and classifier training, which can utilize the reverse propagation mechanism of the deep network to complete the optimization and feature extraction of the classifier simultaneously. In the simulation experiment, the classification accuracy of the network frame class reaches 78.61%, compared with other methods, it effectively improves the target recognition accuracy. The proposed framework can effectively identify underwater targets, with good recognition accuracy, and have a modular structure, without complex preprocessing.