Infrared Aircraft Detection Based on Feature Enhancement and Sufficient Sample Learning
Aiming at the problem that the deep learning single-stage detection algorithm has insufficient feature extraction ability and insufficient sample learning for infrared aircraft targets,a target detection algorithm is proposed based on Feature-Enhanced Global Context Mechanism(FEGCM)and sufficient sample learning.FEGCM can obtain feature images containing both global and local information,and the target feature extracting ability of feature extraction network is improved.By adding modulation factor into Focal Loss,it makes full use of some easy negative samples containing target characteristics on the basis of paying attention to the learning of difficult negative samples,so that the samples are learned sufficiently,which helps the detection algorithm learn more meaningful target features.Experiments show that the proposed algorithm has a mAP50 of 96.9％on the self-made infrared aircraft dataset,which can effectively realize infrared aircraft target detection.
万方数据
维普
