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Acta Aeronautica et Astronautica Sinica ›› 2024, Vol. 45 ›› Issue (20): 630702.doi: 10.7527/S1000-6893.2024.30702

• Aeronautics Computing and Simulation Technique • Previous Articles    

Scene abstract semantic synthesis model and its application in infrared dim and small target detection

Shaoyi LI1, Yaqi ZHANG1, Yue CHENG2, Xi YANG1(), Liang ZHANG3, Jian LIN1, Zhongjie MENG1   

  1. 1.School of Astronautics,Northwestern Polytechnical University,Xi’an  710072,China
    2.Xi’an Aeronautics Computing Technique Research Institute,AVIC,Xi’an  710065,China
    3.China Airborne Missile Academy,Luoyang  471009,China
  • Received:2024-05-20 Revised:2024-06-11 Accepted:2024-06-28 Online:2024-07-11 Published:2024-07-01
  • Contact: Xi YANG E-mail:nwpuyx@163.com
  • Supported by:
    National Natural Science Foundation of China(62273279);Postdoctoral Fellowship Program of CPSF(GZC20232105);Sponsored by Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University(CX2024043)

Abstract:

Accurate and stable detection of dim and small targets in complex, multi-task, and cross-domain environments plays a critical role in infrared early warning, search and tracking, and precision guidance. Current algorithms overly rely on manually designed strategies and prior knowledge, focusing primarily on target information extraction while insufficiently mining and utilizing scene information. This results in limited performance and inadequate environmental adaptability. To address these issues, this paper proposes a dim and small target detection method based on scene abstract semantic synthesis. First, four scene abstract semantic synthesis models are designed using cross-attention, Siamese networks, extended semantic graphs, and self-learning dual-channel methods. Next, based on these four semantic synthesis models, a dim and small target detection network based on scene semantic synthesis is designed. This network incorporates scene category semantic information into the infrared dim and small target detection process to achieve detection in various complex backgrounds. Finally, experimental results show that the proposed self-learning dual-channel semantic synthesis-based dim and small target detection algorithm achieves an accuracy of 84.24% and a recall rate of 89.68%.

Key words: scene semantics, semantic synthesis, image processing, deep learning, infrared dim and small target detection

CLC Number: