Identification Algorithm Based on Key-Point Detection Network for Vital Parts of Infrared Aerial Target

Abstract

The precision strike capability of an infrared-guided air-to-air missile to target the vital parts of a fighter is key to precision-guidance weapons. The traditional image processing algorithms select features and designs classifiers according to human prior knowledge, but this has some limitations. Therefore we propose an algorithm for identifying the vital parts of an infrared aerial target based on key-point detection networks. The algorithm uses the end-to-end deep learning network architecture and combines illumination with texture. The data set is augmented and enhanced in terms of lighting, texture and deformation. The entire image information is preprocessed simply as input, and a loss function with constraints is constructed and iterated with an optimization algorithm. Compared with the conventional algorithms with the same training, the average recognition rate of the trained network model increases by 10%. The vital parts of the infrared aerial target are identified at the speed of ≤ 10 ms/frame. The accuracy of recognition of the 4 vital parts proposed by us is more than 80%.