结合批规范化层的深度学习模型在水中目标识别中的应用

Application of deep learning model combined with batch normalization layer in underwater target recognition

针对深度学习模型在训练水声样本数据时会出现稳定性差进而导致分类识别效果不佳的问题,从网络的局部连接、空间位置排列以及模型设计的角度出发,研究在原有一维序列卷积核与一维序列池化的基础上,引入批规范化层,构建了深度学习网络模型。通过归一化处理,达到加速网络模型的收敛过程以及提高训练过程中的稳定性的目的。最终为验证该模型的有效性,对3类水声目标样本数据进行网络训练和模型验证,证明该模型对水声目标数据分类识别有一定程度的性能提升。

In view of the poor stability of deep learning in training underwater acoustic targets,resulting in poor classification and recognition performance,from the perspectives of local connectivity,spatial arrangement,and model design of the network,based on the original one-dimensional sequence convolution kernel and one-dimensional sequence pooling,this paper introduces batch normalization layer to build a deep learning network model.By normalizing,the goals of accelerating the convergence process of the network model and improving the stability during the training process are achieved.To verify the effectiveness of the model,network training and model validation are carried out on sample data of three types of underwater acoustic targets,which proves that the model also has a certain degree of performance improvement in improving the classification and recognition performance of underwater acoustic target data.