Target Recognition Method of Laser Imaging Fuze Based on Deep Transfer Learning

Abstract

In the process of high-speed missile-target intersection, target recognition technology is a very important part of the laser fuze system. All of existing target recognition methods have the problems of difficulty in obtaining target data, resulting in fewer sample and low target recognition accuracy. In this paper, we use the deep migration learning method on the target recognition task of laser imaging fuze, and the particle filter are used to optimize and improve it to increase the detection accuracy and speed, so as to meet the real-time requirements of target recognition of laser imaging fuze. The experiment uses the UG NX11.0 software to simulate the various flight attitudes of the fighter jets when the J- 20, clouds, and fog are scanned at different angles of the laser line. The obtained partial images are used to test the recognition method. The results show that, compared with the traditional Faster R-CNN, the improved Faster R-CNN reduces the detection time and achieve 97.3% recognition accuracy and 23 fps speed, even basically achieving real-time laser fuze target recognition.