ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Infrared aircraft target detection method based on multi-level feature enhancement fusion
Received date: 2023-06-26
Revised date: 2023-07-17
Accepted date: 2023-08-30
Online published: 2023-09-06
Supported by
National Natural Science Foundation of China(62075239)
Infrared aircraft target detection and recognition has broad prospects for applications in the fields of unmanned reconnaissance， target precision guidance， and flight monitoring. To solve the difficulty in accurate recognition caused by few available features and small difference between classes in infrared aircraft target detection and recognition， a Multi-level Feature Enhanced Fusion Network model （MFEFNet） is proposed. Firstly， a Local and Global Feature Enhancement fusion module （LGFE） is designed. By designing the coordinate attention mechanism and the global pixel attention mechanism， the module enhances and merges the deep semantic features and the low-level detail features. The module implements the top-down mode to dynamically guide the low-level local feature enhancement with deep semantics， and then adaptively strengthens the feature map representation of infrared aircraft target information. Then， a Global Extension Module （GEM） is designed. Based on the feature pyramid， the module further aggregates the multi-scale context information of the intermediate feature map to improve the ability of the network to identify multi-class targets. Finally， the GEM module and LGFE module are cascaded to maintain the long-distance dependence of features and produce a joint effect， which further improves the ability to make feature characterization and target classification decisions and effectively solves the problems of missing features and small differences between classes of infrared aircraft targets. In addition， this paper develops a new ground infrared aircraft dataset based on transfer learning， and analyzes the consistency between transfer data and real infrared data at the feature level by using Gram distance. The experimental results on the ground infrared aircraft dataset show that each module of MFEFNet is effective. Compared with other advanced algorithms， the algorithm proposed can improve the mean Average Precision （mAP） of ground infrared aircraft target recognition by more than 4.3%， and has achieved obvious advantages in recognition accuracy.
Yi ZHANG , Yan ZHANG , Yu ZHANG , Yong ZHANG , Di LIU . Infrared aircraft target detection method based on multi-level feature enhancement fusion[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(22) : 629220 -629220 . DOI: 10.7527/S1000-6893.2023.29220
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