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YANG Xin, WANG Gang, LI Liang, LI Shaogang, GAO Jin, WANG Yizheng. Civil Drone Detection Based on Deep Convolutional Neural Networks: a Survey[J]. Infrared Technology , 2022, 44(11): 1119-1131.
Citation: YANG Xin, WANG Gang, LI Liang, LI Shaogang, GAO Jin, WANG Yizheng. Civil Drone Detection Based on Deep Convolutional Neural Networks: a Survey[J]. Infrared Technology , 2022, 44(11): 1119-1131.
shu

Civil Drone Detection Based on Deep Convolutional Neural Networks: a Survey

  • 1.

    University of South China, Hengyang 421001, China

  • 2.

    Institute of Military Cognition and Brain Sciences, Academy of Military Sciences, Beijing 100850, China

  • 3.

    Chinese Institute for Brain Research, Beijing 102206, China

  • 4.

    Institute of Automation, China Academy of Sciences, Beijing 100190, China

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  • Received Date: September 02, 2021
  • Revised Date: October 12, 2021
    Graphical Abstract
    • Abstract
  • Vision-based early warnings against civil drones are crucial in the field of public security and are also challenging in visual object detection. Because conventional target detection methods built on handcrafted features are limited in terms of high-level semantic feature representations, methods based on deep convolutional neural networks (DCNNs) have facilitated the main trend in target detection over the past several years. Focusing on the development of civil drone-detection technology based on DCNNs, this paper introduces the advancements in DCNN-based object detection algorithms, including two-stage and one-stage algorithms. Subsequently, existing drone-detection methods developed for still images and videos are summarized separately. In particular, motion information extraction approaches to drone detection are investigated. Furthermore, the main bottlenecks in drone detection are discussed. Finally, potentially promising solutions and future development directions in the drone-detection field are presented.
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    • References (82)
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