基于代价敏感剪枝卷积神经网络的弹道目标识别

为降低弹道目标整体误识别代价，提出了基于代价敏感剪枝（CSP）一维卷积神经网络（1D-CNN）的弹道目标高分辨距离像识别方法。首先，基于彩票假设提出了同时以降低模型复杂度和误识别代价为目标的统一框架；然后，在此基础上，提出了基于人工蜂群算法的网络结构无梯度优化方法，以网络结构搜索的方式自动地寻找1D-CNN的代价敏感子网络，即代价敏感剪枝；最后，为了使代价敏感子网络在微调过程中仍以最小化误识别代价为目标，提出了一种代价敏感交叉熵（CSCE）损失函数对训练进行优化，使代价敏感子网络侧重对误识别代价较高的类别正确分类来进一步降低整体误识别代价。实验结果表明:结合CSP和CSCE损失函数的1D-CNN能在保持较高的识别正确率的前提下，相比传统的1D-CNN具有更低的整体误识别代价，且降低了50%以上的计算复杂度。 

Ballistic target recognition based on cost-sensitively pruned convolutional neural network

Aimed at reducing the overall misrecognition cost of ballistic targets, A One-Dimensional Convolutional Neural Network (1D-CNN) based on Cost-Sensitively Pruning (CSP) is proposed for ballistic target high-resolution range profile recognition. Firstly, based on the lottery ticket hypothesis, a unified framework is proposed to reduce the model complexity and overall misidentification cost concurrently. On this basis, a gradient-free optimization method of network structure based on artificial bee colony algorithm is proposed, which can automatically find the cost-sensitive subnetwork of 1D-CNN, namely, cost-sensitively pruning. Finally, in order to make the cost-sensitive sub-network still be aimed at minimizing the cost of misrecognition during the fine-tuning process, a novel Cost-Sensitive Cross Entropy (CSCE) loss function is proposed to optimize the training, so that the cost-sensitive sub-network focuses more on correctly classifying the categories with higher misrecognition cost to further reduce the overall misrecognition cost. The experimental results show that the proposed 1D-CNN combined with the CSP and CSCE loss function has a lower overall misrecognition cost than traditional 1D-CNN under the premise of maintaining a higher recognition accuracy, and reduces the computational complexity by more than 50% as well.