Ship Target Recognition Based on Highly Efficient Scalable Improved Residual Structure Neural Network
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摘要: 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。Abstract: 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.
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表 1 模型A各阶段参数情况
阶段 输出 结构 参数个数 初始卷积层 128×1×9 7×1, 9, s=2 99 左侧支路 右侧支路 卷积模块1 64×1×18 1×1, 9
3×1, 3, s=2, x=3
1×1, 121×1, 15, s=2 585 卷积模块2 32×1×36 1×1, 18
3×1, 6, s=2, x=3
1×1, 241×1, 30, s=2 1980 卷积模块3 16×1×72 1×1, 36
3×1, 12, s=2, x=3
1×1, 481×1, 60, s=2 7200 卷积模块4 8×1×144 1×1, 72
3×1, 24, s=2, x=3
1×1, 961×1, 120, s=2 27360 全连接层1 144 全局平均池化+全局最大值池化 0 全连接层2 2 288 输出层 13 SL+CL 26 参数总数 37538 
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表 2 不同复杂度模型在不同信噪比数据集下的识别准确率(%)
模型名称 识别时间(μs) 信噪比(dB) 0 5 10 15 模型A 258 60.42 89.41 98.21 99.83 模型B 326 72.95 94.41 99.15 99.89 模型C 323 73.78 93.71 99.07 99.86
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表 3 CNN模型结构和参数明细
阶段 输出维度 网络结构 参数个数 卷积层1 256×1×8 3×1, 8, s=1 64 池化层1 128×1×8 2×1, s=2 0 卷积层2 128×1×16 3×1, 16, s=1 464 池化层2 64×1×16 2×1, s=2 0 卷积层3 64×1×32 3×1, 32, s=1 1696 池化层3 32×1×32 2×1, s=2 0 卷积层4 32×1×64 3×1, 64, s=1 6464 池化层4 16×1×64 2×1, s=2 0 卷积层5 16×1×64 1×1, 64, s=1 4416 池化层5 8×1×64 2×1, s=2 0 全连接层1 64 32832 全连接层2 2 130 输出层 13 SL 39 参数总数 46105
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表 4 SDSAE&KNN模型结构和参数明细
阶段 输出维度 参数个数 隐藏层1 150×1 38550 隐藏层2 100×1 15100 隐藏层3 50×1 5050 隐藏层4 10×1 510 参数总数 59210
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表 5 SCAE模型结构和参数明细
阶段 输出维度 网络结构 参数个数 卷积层1 256×1×128 5×1, 128, s=1 768 池化层1 128×1×128 2×1, s=2 0 卷积层2 128×1×64 5×1, 64, s=1 41024 池化层2 64×1×64 2×1, s=2 0 卷积层3 64×1×32 3×1, 32, s=1 6176 池化层3 32×1×32 2×1, s=2 0 卷积层4 32×1×16 3×1, 16, s=1 1552 池化层4 16×1×16 2×1, s=2 0 卷积层5 16×1×8 1×1, 8, s=1 136 池化层5 8×1×8 2×1, s=2 0 输出层 13 SL 845 参数总数 50501
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表 6 不同信噪比条件下本节模型与对比模型识别准确率(%)
模型名称 识别时间(μs) 信噪比(dB) 0 5 10 15 模型A 258 60.42 89.41 98.21 99.83 CNN 69 58.22 86.91 95.51 98.79 SCAE 47 54.78 86.58 94.44 98.78 SDSAE&KNN 68 46.50 83.94 93.44 98.65
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