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.