Laser & Infrared, Volume. 55, Issue 2, 288(2025)

Airborne infrared UXO target detection method based on SAE-YOLOv5

LIU Zi-yu1, ZHAO Xu1、*, LI Lian-peng1, and XU Xue-ping2
Author Affiliations
  • 1Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science and Technology University, Beijing 100192, China
  • 2Beijing Honda Hechuang Defense Technology Research Institute Co., Ltd., Beijing 100048, China
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    References(7)

    [1] [1] T. Frey, Prop., Explos., Pyrotech. UXO and environmental risk factors impacting EOD operations in German waters[J]. Propellants Explosives Pyrotechnics, 2024, 49(4): e2300206.

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    [5] [5] Cho S, Ma J, A. Yakimenko O. Aerial multi-spectral AI-based detection system for unexploded ordnance[J]. Defence Technology, 2023, 27(9): 24-37.

    [14] [14] Arola, S., et al. Vision-based deep learning for UAVs collaboration[C]//Conference on Unmanned Systems Technology XXI, 2019: 11021.

    [15] [15] Qi, Z., et al., First results from Drone-based transient electromagnetic survey to map and detect unexploded ordnance[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(12): 2055-2059.

    [16] [16] Andrew Brock, et al. Large scale gan training for high fidelity natural image synthesis[J]. arXiv: 1809.11096.

    [20] [20] Lowe D G. Distinctive image features from scale invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

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    LIU Zi-yu, ZHAO Xu, LI Lian-peng, XU Xue-ping. Airborne infrared UXO target detection method based on SAE-YOLOv5[J]. Laser & Infrared, 2025, 55(2): 288

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    Paper Information

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    Received: Mar. 25, 2024

    Accepted: Apr. 3, 2025

    Published Online: Apr. 3, 2025

    The Author Email: ZHAO Xu (zhaoxu@bistu.edu.cn)

    DOI:10.3969/j.issn.1001-5078.2025.019

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