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

基于关键点检测网络的空中红外目标要害部位识别算法

School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China

Received: 21 March 2020

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%.

摘要

红外制导空空导弹对战机要害部位的定向精确打击能力是精确制导武器的关键技术之一。针对传统图像处理算法中根据人的先验知识进行特征选择、设计分类器方法的局限性，提出一种基于关键点检测卷积网络的空中红外目标要害部位检测算法。该算法采用端对端的深度学习网络结构，结合数据集对光照、纹理、形变方面进行扩充增强，将整幅图像信息简单预处理后作为输入，构建含约束条件的损失函数并利用优化算法进行迭代。相较于同样训练批次的常规方法，训练得到的网络模型的平均识别率提高了10%，能够更准确地识别红外空中目标要害部位，对空中红外目标的4个要害部位识别的准确率达到80%以上。