基于深度卷积神经网络的小型民用无人机检测研究进展

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

小型民用无人机预警探测是公共安全领域的热点问题,也是视觉目标检测领域的研究难点。采用手工特征的经典目标检测方法在语义信息的提取和表征方面存在局限性,因此基于深度卷积神经网络的目标检测方法在近年已成为业内主流技术手段。围绕基于深度卷积神经网络的小型民用无人机检测技术发展现状,本文介绍了计算机视觉目标检测领域中基于深度卷积神经网络的双阶段算法和单阶段检测算法,针对小型无人机检测任务分别总结了面向静态图像和视频数据的无人机目标检测方法,进而探讨了无人机视觉检测中亟待解决的瓶颈性问题,最后对该领域研究的未来发展趋势进行了讨论和展望。

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.