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基于深度卷积神经网络的小型民用无人机检测研究进展

杨欣 王刚 李椋 李邵港 高晋 王以政

杨欣, 王刚, 李椋, 李邵港, 高晋, 王以政. 基于深度卷积神经网络的小型民用无人机检测研究进展[J]. 红外技术, 2022, 44(11): 1119-1131.
引用本文: 杨欣, 王刚, 李椋, 李邵港, 高晋, 王以政. 基于深度卷积神经网络的小型民用无人机检测研究进展[J]. 红外技术, 2022, 44(11): 1119-1131.
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.

基于深度卷积神经网络的小型民用无人机检测研究进展

基金项目: 

北京市自然科学基金 4214060

国家自然科学基金 62102443

详细信息
    作者简介:

    杨欣(1997-),女,硕士研究生,研究方向为视频目标检测。E-mail: yangxinioi@163.com

    通讯作者:

    王刚(1988-),男,副研究员,研究方向为类脑视觉感知。E-mail: g_wang@foxmail.com

  • 中图分类号: TP391.4

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

  • 摘要: 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。
  • 图  1  以R-CNN算法[15]为例的双阶段目标检测算法示意图

    Figure  1.  Flowchart of the two-stage object detection algorithm, taking R-CNN [15] as an example

    图  2  以YOLO算法[33]为例的单阶段目标检测算法流程示意图

    Figure  2.  Flowchart of the one-stage object detection algorithm, taking YOLO [33] as an example

    图  3  Anti-UAV2020数据集示例图片(左列为可见光图像,右列为红外图像)

    Figure  3.  Example images in the Anti-UAV2020 dataset (The left column shows RGB images, and the right column shows infrared images)

    图  4  Drone-vs-Bird Detection Challenge[46]数据集示例图片

    Figure  4.  Example images in the Drone-vs-Bird Detection Challenge[46] Dataset

    图  5  Anti-drone Dataset[47]中示例图片

    Figure  5.  Example images in the Anti-drone dataset[47]

    图  6  UAV dataset[48]示例图片

    Figure  6.  Example images in the UAV dataset[48]

    图  7  超分辨率增强模块结合Faster R-CNN模型的无人机检测算法流程图[49]

    Figure  7.  Flowchart of the UAV detection algorithm[49] combined with the super-resolution enhancement module and the Faster R-CNN model

    图  8  基于多尺度YOLOv3的UAVDet算法[48]流程示意图

    Figure  8.  Flowchart of the UAVDet algorithm[48] that is based on the multi-scale YOLOv3 structure

    图  9  FlowNet[72]模型计算光流过程示意图

    Figure  9.  Diagram of the FlowNet[72] for calculating optical flow

    图  10  运动补偿的目标检测算法流程[77]

    Figure  10.  Flow chart of the object detection algorithm incorporating motion compensation[77]

    图  11  无人机检测的难点和瓶颈性问题示例图像

    注:第一行:目标小尺寸且缺乏外观信息[47, 55, 62];第二行:背景复杂多样[47-48];第三行:目标尺度异质性问题[53]

    Figure  11.  Image examples to demonstrate difficulties and bottlenecks in drone detection

    Note: Row 1: Targets that are small and weak in appearance information[47, 55, 62]; Row 2: Targets in complex and diverse backgrounds[47-48]; Row 3: Targets that have heterogeneous scales [53])

    表  1  视觉目标检测领域代表性算法归纳

    Table  1.   Summary of representative algorithms in the visual object detection field

    Model Year Backbone Characteristics
    Two-stage R-CNN[15] 2014 AlexNet[16] Integrate CNN classification and proposal generation; need multi-stage training; time-consuming and space-consuming.
    SPPNet[17] 2015 ZFNet[19] Introduce the spatial pyramid pooling (SPP) into CNNs.
    Fast R-CNN[18] 2015 AlexNet、VGG16[20] Introduce regions of interest (RoIs) pooling layer; difficult to achieve real-time detection.
    Faster R-CNN[21] 2015 ZFNet、VGG Introducing region proposal network (RPN) to generate high-quality proposals; complex training procedures and poor real-time performance.
    ION[22] 2016 IRNN[23] Improve performance on small object detection by employing context and multi-scale skip pooling.
    R-FCN[24] 2016 ResNet101[25] Apply the fully convolutional neural network (FCN) to Faster R-CNN to share the computation of the entire network, improving detection speed.
    FPN[26] 2017 ResNet101 Propose a feature pyramid model to handle scale variation issues in object detection.
    Mask R-CNN[27] 2018 ResNeXt[28]、FPN Add parallel branches to extend Faster R-CNN to achieve object segmentation, which cannot be detected in real-time.
    PANet[29] 2018 FPN Bottom-up enhancement path and adaptive feature pooling are introduced.
    TridentNet[30] 2019 ResNet101 Elucidating the effect of receptive field on objects of different sizes in object detection tasks.
    CPNDet[31] 2020 Hourglass104[32] Generate anchor-free proposals; two-step classification for filtering proposals.
    One-stage YOLOv1[33] 2016 GoogLeNet[34] End-to-end real-time detection does not produce proposals but has poor detection accuracy and difficult to detect small cluster objects.
    SSD[35] 2016 VGG16 Combined with CNN and YOLOv1 model, SSD detects on multi-scale layers, which is faster and more accurate than YOLOv1.
    YOLOv2[36] 2016 DarkNet19 Propose DarkNet19 to achieve high precision and high speed, but it is still difficult to detect small objects.
    RetinaNet[37] 2018 ResNeXt101+FPN Proposed focal loss function to solve the extreme foreground-background class imbalance problem.
    YOLOv3[38] 2018 DarkNet53 Improving performance on small objects by multi-scale detection.
    STDN[39] 2018 DenseNet169[40] Resolve multi-scale objects by employing a scale transformation module.
    CornerNet[41] 2019 Hourglass104 Regard the object detection task as a key point detection problem,by inferencing two key points (upper left and lower right corners) as the prediction box.
    YOLOv4[42] 2020 CSPDarknet53 Faster and more accurate object detection in terms of mosaic data augmentation and self-adversarial training tips.
    DETR[43] 2020 ResNet101 Introduce transformer structure to object detection field, but the performance for small targets needs to be improved.
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出版历程
  • 收稿日期:  2021-09-03
  • 修回日期:  2021-10-13
  • 刊出日期:  2022-11-20

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