基于双流对称特征融合网络模型的海洋船舶目标识别

The marine ship target recognition has important strategic significance in civil and military fields, this paper proposes a two-stream symmetric feature fusion convolutional neural network model based on attention mechanism for visible and infrared images, in order to improve the comprehensive recognition performance of ship targets in complex perception environment. The model uses the two-stream symmetric network to extract visible and infrared image features in parallel. By constructing a multi-level fusion layer based on cascade average fusion, the complementary information of visible and infrared modes is effectively used to obtain more comprehensive ship feature description. At the same time, the spatial attention mechanism is introduced into the feature fusion module to enhance the response of key regions in the fusion feature map and further improve the overall recognition performance of the model. A series of experiments on the VAIS real data set have proved the effectiveness of the model, its recognition accuracy can reach 87.24%, and its comprehensive performance is significantly superior to the existing methods.