Research on Fast Recognition Algorithm of Real-time Missile Terminal Guidance Infrared Target Based on Deep CNN

In this paper, a fast recognition algorithm based on Deep Convolutional Neural Network (DCNN) is proposed and studied for the speed and accuracy of infrared target recognition in missile terminal guidance system. This algorithm makes full use of the advantages of deep learning model in feature extraction and pattern recognition, combined with the unique characteristics of infrared imaging, and designs and trains a deep convolutional neural network model suitable for the infrared target recognition task of missile terminal guidance. By optimizing network structure, adopting advanced training strategy and fusing multi-scale and multi-view infrared image information, the constructed model can realize efficient and accurate recognition of infrared targets under complex background, and meet the real-time requirements. The experimental results show that the algorithm not only significantly improves the accuracy of target recognition, but also ensures the ability to process a large amount of infrared image data in real time under the condition of high-speed flight, so as to effectively improve the strike accuracy and combat efficiency of the missile terminal guidance system, which has important theoretical value and practical application significance for promoting the development of precision guidance weapon technology in China.