基于高效可扩展改进残差结构神经网络的舰船目标识别技术

神经网络的深度在一定范围内与识别效果成正相关，为解决超出范围后网络层数增加识别准确率却下降的模型饱和问题，该文提出一种具有高效的微块内部结构和残差网络结构的神经网络模型，用于对舰船目标基于高分辨距离像的分类识别。该方法利用具有小尺度卷积核的卷积模块提取目标的稳定可分特征，同时利用联合损失函数约束目标特征的类内距离提高识别能力。仿真结果表明，该模型相比于其他常见网络结构，在模型参数更少的情况下，识别效果更好，同时具有较强的噪声鲁棒性。

Ship Target Recognition Based on Highly Efficient Scalable Improved Residual Structure Neural Network

The depth of neural network is positively correlated with the recognition effect in a certain range. In order to solve the problem that model recognition accuracy decreases when the number of network layers increases after exceeding the range. A neural network model with efficient micro internal blocks structure and residual network structure is proposed, which is used for recognition of ship targets based on High Range Resolution Profile (HRRP) data. In this method, the convolution module with a small scale convolution kernel is used to extract automatically the stable and separable features of target. And the intra-class distance of the target is constrained by the joint loss function to improve the recognition ability. Simulation results show that compared with other common network structures, this model has better recognition performance and stronger noise robustness with fewer model parameters.