中圖分類號：TP183;TP391文獻標識碼：ADOI:10.19358/j.issn.2097-1788.2024.04.007
引用格式：孫悅，彭圓，賈連徽，等.結合批規范化層的深度學習模型在水中目標識別中的應用［J］.網絡安全與數據治理，2024，43（4）：41-45,52.

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

Abstract： 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.

Key words : underwater acoustic target; deep learning; classification; network model