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

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^{1, 2,},
王刚^{2, 3, ,},
李椋²,
李邵港^{1, 2},
高晋⁴,
王以政²

1.
南华大学, 湖南衡阳 421001
2.
军事科学院军事认知与脑科学研究所, 北京 100850
3.
北京脑科学与类脑研究中心, 北京 102206
4.
中国科学院自动化研究所, 北京 100190

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
计量
- 文章访问数: 207
- HTML全文浏览量: 52
- PDF下载量: 75
- 被引次数: 0
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

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

YANG Xin^{1, 2
,},
WANG Gang^{2, 3
, ,},
LI Liang²,
LI Shaogang^{1, 2},
GAO Jin⁴,
WANG Yizheng²

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

摘要

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。
- 计算机视觉 /
- 目标检测 /
- 视频目标检测 /
- 无人机检测 /
- 深度卷积神经网络
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.
- computer vision /
- object detection /
- video object detection /
- civil drone detection /
- deep convolutional neural networks

HTML全文

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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)

下载: 全尺寸图片幻灯片

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

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图 5 Anti-drone Dataset^[47]中示例图片

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

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图 6 UAV dataset^[48]示例图片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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]

下载: 全尺寸图片幻灯片

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])

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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.

下载: 导出CSV

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