• Journal of Applied Optics
  • Vol. 45, Issue 4, 723 (2024)
Haiyong CHEN1, Dengbin LIU1, and Xingwei YAN2、3、*
Author Affiliations
  • 1School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300401, China
  • 2College of Electronic Science, National University of Defense Technology, Changsha 410073, China
  • 3Tianjin Institute of Advanced Technology, Tianjin 300459, China
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    DOI: 10.5768/JAO202445.0402001 Cite this Article
    Haiyong CHEN, Dengbin LIU, Xingwei YAN. Infrared image UAV target detection algorithm based on IDOU-YOLO[J]. Journal of Applied Optics, 2024, 45(4): 723 Copy Citation Text show less

    Abstract

    Low-altitude small unmanned aerial vehicle (UAV) frequently invade sensitive areas, which posing a serious threat to national and social security. Aiming at the problems such as high missed detection rate and insufficient detection accuracy for UAV target detection based on thermal imaging, the infrared detection of UAV-YOLO (IDOU-YOLO) algorithm model was proposed. A cross-scale fusion feature pyramid mechanism was constructed to fully explore the feature space information, focus on the cross-scale information fusion and the rich information representation ability of the model, and enhance the target recognition ability. At the same time, the bounding box loss function Scylla IoU (SIoU) was introduced to improve the detection accuracy and accelerate the convergence speed of the model in the training process. The experimental results show that the precision, recall, F1 score, mAP@0.5 and mAP@0.5:0.95 reach 99.2%, 96.3%, 97.7%, 98.4% and 70.2%, respectively, which indicates that the IDOU-YOLO model improves the detection and recognition ability of UAV targets in various scenarios, and can better meet the application requirements of anti-UAV systems.
    Haiyong CHEN, Dengbin LIU, Xingwei YAN. Infrared image UAV target detection algorithm based on IDOU-YOLO[J]. Journal of Applied Optics, 2024, 45(4): 723
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