Enhancement Dataset for Low Altitude Unmanned Aerial Vehicle Detection

In recent years,the number of incidents involved with unmanned aerial vehicles(UAVs)has increased conspicuously,resulting in an increasingly urgent demand for developing anti-UAV systems. The vast requirements of high detection accuracy with respect to low altitude UAVs are put forward. In addition,the methods of UAV detection based on deep learning are of great potential in low altitude UAV detection. However,such methods need high-quality datasets to cope with the problem of high false alarm rate(FAR)and high missing alarm rate(MAR)in low altitude UAV detection,special high-quality low altitude UAV detection dataset is still lacking. A handful of known datasets for UAV detection have been rejected by their proposers for authorization and are of poor quality. In this paper,a comprehensive enhanced dataset containing UAVs and jamming objects is proposed. A large number of high-definition UAV images are obtained through real world shooting, web crawler, and data enhancement.Moreover,to cope with the challenge of low altitude UAV detection in complex backgrounds and long distance,as well as the puzzle caused by jamming objects,the noise with jamming characteristics is added to the dataset. Finally,the dataset is trained,validated,and tested by four mainstream deep learning models. The results indicate that by using data enhancement,adding noise contained jamming objects and images of UAV with complex backgrounds and long distance,the accuracy of UAV detection can be significantly improved. This work will promote the development of anti-UAV systems deeply,and more convincing evaluation criteria are provided for models optimization for UAV detection.

A cooperative detection game:UAV swarm vs.one fast intruder

This paper studies a special defense game using unmanned aerial vehicle(UAV)swarm against a fast intruder.The fast intruder applies an offensive strategy based on the artificial potential field method and Apollonius circle to scout a certain destination.As defenders,the UAVs are arranged into three layers:the forward layer,the midfield layer and the back layer.The co-defense mechanism,including the role derivation method of UAV swarm and a guidance law based on the co-defense front point,is introduced for UAV swarm to co-detect the intruder.Besides,five formations are designed for comparative analysis when ten UAVs are applied.Through Monte Carlo experiments and ablation experiment,the effectiveness of the proposed co-defense method has been verified.

Improved Weighted Local Contrast Method for Infrared Small Target Detection

In order to address the problem of high false alarm rate and low probabilities of infrared small target detection in complex low-altitude background,an infrared small target detection method based on improved weighted local contrast is proposed in this paper.First,the ratio information between the target and local background is utilized as an enhancement factor.The local contrast is calculated by incorporating the heterogeneity between the target and local background.Then,a local product weighted method is designed based on the spatial dissimilarity between target and background to further enhance target while suppressing background.Finally,the location of target is obtained by adaptive threshold segmentation.As experimental results demonstrate,the method shows superior performance in several evaluation metrics compared with six existing algorithms on different datasets containing targets such as unmanned aerial vehicles(UAV).

Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.

An Edge-Boxes-Based Intruder Detection Algorithm for UAV Sense and Avoid System

With the great development of unmanned aircraft system(UAS)over the last decade,sense and avoid(SAA)system has been a crucial technology for integrating unmanned aircraft vehicle(UAV)into national airspace with reliable and safe operations.This paper mainly focuses on intruder detection for SAA system.A robust algorithm based on the combination of edge-boxes and spatial pyramid matching using sparse coding(sc-SPM)is presented.The algorithm is composed of three stages.First,edge-boxes method is adopted to obtain a large number of proposals;Second,the optimization program is presented to obtain intruder area-of-interest(ROI)regions;Third,sc-SPM is employed for feature representation of ROI regions and support vector machines(SVM)is adopted to detect the intruder.The algorithm is evaluated under different weather conditions.The recall reaches to 0.95 in dawn and sunny weather and 0.9 in cloudy weather.The experimental results indicate that the intruder detection algorithm is effective and robust with various weather under complex background.

Distributed tracking control of unmanned aerial vehicles under wind disturbance and model uncertainty

A distributed robust method is developed for cooperative tracking control of unmanned aerial vehicles under unknown wind disturbance and model uncertainty. The communication network among vehicles is a directed graph with switching topology. Each vehicle can only share its states with its neighbors. Dynamics of the vehicles are nonlinear and affected by the wind disturbance and model uncertainty. Feedback linearization is adopted to transform the dynamics of vehicles into linear systems. To account for the wind disturbance and model uncertainty, a robust controller is designed for each vehicle such that all vehicles ultimately synchronize to the virtual leader in the three-dimensional path. It is theoretically shown that the position states of the vehicles will converge to that of the virtual leader if the communication network has a directed spanning tree rooted at the virtual leader. Furthermore, the robust controller is extended to address the formation control problem. Simulation examples are also given to illustrate the effectiveness of the proposed method. © 2016 Beijing Institute of Aerospace Information.

Small objects detection in UAV aerial images based on improved Faster R-CNN

In order to solve the problem of small objects detection in unmanned aerial vehicle(UAV)aerial images with complex background,a general detection method for multi-scale small objects based on Faster region-based convolutional neural network(Faster R-CNN)is proposed.The bird’s nest on the high-voltage tower is taken as the research object.Firstly,we use the improved convolutional neural network ResNet101 to extract object features,and then use multi-scale sliding windows to obtain the object region proposals on the convolution feature maps with different resolutions.Finally,a deconvolution operation is added to further enhance the selected feature map with higher resolution,and then it taken as a feature mapping layer of the region proposals passing to the object detection sub-network.The detection results of the bird’s nest in UAV aerial images show that the proposed method can precisely detect small objects in aerial images.

Exploring Image Generation for UAV Change Detection

Change detection(CD)is becoming indispensable for unmanned aerial vehicles(UAVs),especially in the domain of water landing,rescue and search.However,even the most advanced models require large amounts of data for model training and testing.Therefore,sufficient labeled images with different imaging conditions are needed.Inspired by computer graphics,we present a cloning method to simulate inland-water scene and collect an auto-labeled simulated dataset.The simulated dataset consists of six challenges to test the effects of dynamic background,weather,and noise on change detection models.Then,we propose an image translation framework that translates simulated images to synthetic images.This framework uses shared parameters(encoder and generator)and 22×22 receptive fields(discriminator)to generate realistic synthetic images as model training sets.The experimental results indicate that:1)different imaging challenges affect the performance of change detection models;2)compared with simulated images,synthetic images can effectively improve the accuracy of supervised models.

基于特征复用机制的航拍图像小目标检测算法

针对无人机(UAV)航拍图像检测存在的小目标检测精度低和模型参数量大的问题,提出轻量高效的航拍图像检测算法FS-YOLO.该算法以YOLOv8s为基准网络,通过降低通道维数和改进网络架构提出轻量的特征提取网络,实现对冗余特征信息的高效复用,在较少的参数量下产生更多特征图,提高模型对特征信息的提取和表达能力,同时显著减小模型大小.在特征融合阶段引入内容感知特征重组模块,加强对小目标显著语义信息的关注,提升网络对航拍图像的检测性能.使用无人机航拍数据集VisDrone进行实验验证,结果表明,所提算法以仅5.48 M的参数量实现了mAP0.5=47.0%的检测精度,比基准算法YOLOv8s的参数量降低了50.7%,精度提升了6.1%.在DIOR数据集上的实验表明,FS-YOLO的泛化能力较强,较其他先进算法更具竞争力.

改进YOLOv8的轻量化无人机目标检测算法

针对无人机目标检测算法计算复杂难以部署,且长尾分布的无人机数据导致检测精度较低的问题,提出了基于改进YOLOv8的轻量化无人机目标检测算法(PC-YOLOv8-n),可均衡网络检测精度与计算量,并对长尾分布数据有一定泛化能力。使用部分卷积层(PConv)替换YOLOv8中的3×3卷积层,对网络进行轻量化处理,解决网络冗余和计算量复杂的问题;融合双通道特征金字塔,增加自上而下的路径,将深层信息与浅层信息进行融合,同层引入轻量化注意力机制,提升网络的特征提取能力;采用均衡焦点损失(EFL)作为类别损失函数,通过均衡尾部类别在网络训练时的梯度权重,增加网络的类别检测能力。实验结果表明,PC-YOLOv8-n在VisDrone2019数据集中具有良好的表现,在mAP50精度上比原始YOLOv8-n算法提高了1.6个百分点,同时模型的参数和计算量分别降低为2.6×10^(6)和7.6 GFLOPs,检测速度达到77.2 FPS。

基于上下文信息与特征细化的无人机小目标检测算法

无人机航拍图像中的目标检测是近年来研究的热点,针对无人机视角下目标小而密集、背景复杂导致检测精度低的问题,提出一种基于上下文信息与特征细化的无人机小目标检测算法。通过上下文特征增强模块,利用多尺度扩张卷积捕获与周围区域像素点的潜在关系,为网络补充上下文信息,并根据不同尺度的特征层自适应生成各层级特征图的输出权重,动态优化特征图表达能力;由于不同特征图细粒度不同,使用特征细化模块来抑制特征融合中冲突信息的干扰,防止小目标特征淹没在冲突信息中;设计了一种带权重的损失函数,加快模型收敛速度,进一步提高小目标检测精度。在VisDrone2021数据集进行大量实验表明,改进后的模型较基准模型mAP50提高8.4个百分点,mAP50:95提高5.9个百分点,FPS为42,有效提高了无人机航拍图像中小目标的检测精度。

高阶深度可分离无人机图像小目标检测算法

当前无人机图像中存在小目标数量众多、背景复杂的特点,目标检测中易造成漏检误检率较高的问题,针对这些问题,提出一种高阶深度可分离无人机图像小目标检测算法.首先,结合CSPNet结构与ConvMixer网络,深度可分离卷积核,获取梯度结合信息,并引入递归门控卷积C3模块,提升模型的高阶空间交互能力,增强网络对小目标的敏感度;其次,检测头采用两个头部进行解耦,分别输出特征图分类和位置信息,加快模型收敛速度;最后,使用边框损失函数EIoU,提高检测框精准度.在VisDrone2019数据集上的实验结果表明,该模型检测精度达到了35.1%,模型漏检率和误检率有明显下降,能够有效地应用于无人机图像小目标检测任务.在DOTA 1.0数据集和HRSID数据集上进行模型泛化能力测试,实验结果表明,该模型具有良好的鲁棒性.

微多普勒辅助的城市环境无人机编队检测方法

针对城市复杂环境下电磁环境复杂、多径杂波和干扰信号密集等现象,传统的无人机(UAV)检测方法通过获取回波信号提取目标多普勒信息进行检测,易受到环境影响导致检测效果不理想。该文提出微多普勒辅助的城市环境无人机编队检测方法,充分利用无人机的微动特征,能够在复杂环境下提高检测精度。首先,参数化建模表征城市复杂环境下无人机旋翼的雷达回波微多普勒信号,利用YOLOv5s检测微多普勒闪烁脉冲,有效提取位置信息;然后,引入雷达信号分选方法的脉冲重复间隔(PRI)变换,分类获得无人机编队数量;最后,利用Kmeans算法验证无人机编队检测方法的准确性。结果表明,所提方法在信噪比2 dB时7架无人机的检测精度高于90%,能够用于城市复杂环境存在干扰脉冲、多径效应、局部脉冲丢失的无人机编队检测。

基于杂波拖尾分布的雷达无人机检测性能分析

固定翼无人机(unmanned aerial vehicle,UAV)给雷达低空监视提出了严峻挑战。分析雷达对固定翼UAV的检测性能,可为雷达UAV检测能力评估和技术升级提供重要参考。本文结合雷达探测低空固定翼UAV外场实测数据,首先分析了低空固定翼UAV雷达接收信号幅度统计分布,采用多项式对杂波拖尾导致的虚警概率进行拟合建模;然后,根据虚警概率分布得到雷达检测门限;进而根据UAV回波+杂波幅度分布理论推导得到雷达检测概率;最后,将理论分析性能与传统性能分析结果、雷达实际检测性能进行对比。结果表明,采用多项式对杂波拖尾导致的虚警概率进行单独建模,由此获得的雷达检测门限精度更高,从而使雷达UAV检测性能分析结果较传统性能分析结果更准确。

基于深度学习的无人机检测和识别研究综述

近年来,由于在各行各业发挥了不可替代作用,无人机产业和应用得到了迅速发展。然而,无人机的“黑飞”、携带危险物品等事件频繁发生,对社会安全构成了严重威胁。因此,无人机的检测和识别变得尤为迫切和必要。随着无人机类型不断地变化,传统的检测与识别方法已不再适应当前需求。深度学习技术的快速发展为无人机检测与识别提供了一种高效且准确的解决方案。深度学习模型具备自主学习特征的能力,能够从大规模数据中提取高级特征,并且在无人机检测与识别任务中表现出色。该模型不仅能够显著提高准确性,还能够适应各种复杂环境和无人机类型。对此,本文综述了基于深度学习的无人机检测与识别技术的最新进展,主要包括基于深度学习的无人机视觉检测和识别、基于深度学习的无人机音频检测和识别、基于深度学习的无人机雷达检测和识别以及基于深度学习的无人机射频检测和识别。最后,对目前无人机检测和识别现存问题进行分析,并展望了未来研究方向。

航母舰载无人机全自动着舰技术特点分析

全自动着舰技术是无人机上舰必须解决的“使能”技术。2013年,美军已完成X-47B无人机验证机在航母上的全自动着舰试验验证,为其发展MQ-25A“黄貂鱼”航母舰载无人机装备奠定了良好的技术基础。在梳理飞机着陆与着舰差异的基础上,给出航母舰载无人机全自动着舰的通用流程;从地位作用、工作范围、系统构架、系统要求、实现手段以及关键技术支撑等角度,对比有人机重点分析了航母舰载无人机全自动着舰的特点,为后续关键技术研究和武器装备建设发展提供参考。

基于微型激光雷达的无人机智能化电力线路巡检技术研究

介绍了一种利用激光探测和测距(LiDAR)进行实时电力线检测的算法,并且对微型激光雷达无人机进行电力线路巡检抽象建模。通过使用平面分析法对巡检现场进行距离的比较来分割点云,并提取一组电力线候选点,这些点被拟合到线段上,线段根据它们的共线性质进一步分组比较。该方法可利用图像的共线特性完成特征检验,并在光线条件差以及线路背景复杂的环境下仍可获得较为可靠的巡检结果。案例中电力线路垂直平均误差最大为0.49 m,其他所有直线的估计平均误差都小于0.07 m,水平拟合时,所有平均误差都在0.14 m以内。验证了所提出基于微型激光雷达的无人机技术进行电力线路智能化巡检方法的有效性与可行性。

基于无人机巡检与深度学习的河道整治施工进度图像识别

长线性小流域治理工程中的河道衬砌、生态护岸等距离长、范围广、布置分散,且工区交通不便,人工巡检费时费力,难以及时掌握工程的整体施工进度形象面貌。提出了基于无人机巡检与深度学习的河道整治施工进度智能图像识别方法,通过定位施工节点(施工区域起点和终点)的位置计算施工进度。首先,建立了施工区域目标检测模型,针对无人机航拍影像进行河道衬砌护岸施工区域的识别以及施工节点的定位;然后,利用尺度不变特征变换(scale-invariant feature transform,SIFT)算法对不同视频帧中的施工节点进行匹配,并基于单目视觉的运动视差法,计算施工节点的实际工区坐标;最后,计算当前衬砌护岸施工进度,并分析进度偏差。结果表明:该方法得到的施工节点定位平均误差为1.026 m,平均相对误差为0.74%,该方法能够较为准确地从航拍图像中识别得到当前衬砌护岸的施工进程,从而实现长线性工区快速巡检,及时掌控现场施工进度,提高工程管理的智能化水平。

城市森林结构多样性预测冠下地面温度的潜力研究

城市森林冠层具有调控城市森林微气候的能力,但现有研究尚未阐明冠层结构对冠下地面温度的影响及其预测潜力。文章基于无人机机载激光雷达(UAV-LiDAR)提取哈尔滨林业示范基地的城市森林冠层结构多样性特征指标,探究单一结构多样性特征对冠下地面温度的影响,以及结构多样性多因子组合对温度的预测潜力。结果表明:1)城市森林结构多样性的8个特征因子与冠下地面温度呈显著相关关系(P<0.05),其中深间隙(DG)、深间隙分数(DGF)、覆盖分数(CF)、间隙分数分布(GFP)表征了结构多样性的覆盖/开放度特征;冠层高度标准差(H_(std))、冠层高度最大值(H_(max))、95%分位点高度(ZQ_(95))表征了高度特征;垂直复杂指数(VCI)表征了异质性特征。2)城市森林冠层结构多样性的覆盖/开放度特征对冠下地面温度的响应更强(R^(2)为0.15~0.5),强于高度指标(R^(2)为0.14~0.19)以及异质性指标(R^(2)=0.14)。3)结合高度指标、覆盖/开放度指标以及异质性指标的多因子预测模型2(R^(2)=0.61,RMSE=0.51,MSE=0.26,AIC=62.74),对于冠下地面温度的预测性能更优。研究明晰了城市森林结构多样性的多因子变量及其特征组合预测冠下地面温度的潜力,为城市森林冠层结构调控内部小气候环境研究提供了科学参考。

基于改进YOLOX的无人机航拍图像小目标检测算法

在无人机上运用目标检测技术具有广泛的应用前景,但和自然拍摄的图像不同,无人机航拍的图像更加复杂,且大多数为小目标。而现有的检测算法缺乏对小目标的特征提取能力,从而导致严重的误检和漏检问题。针对上述问题,提出一种基于YOLOX框架的高效的无人机小目标检测算法。首先,在特征融合网络中增加一层检测微小目标的特征融合结构,通过利用浅层特征图中丰富的位置信息和轮廓信息来加强网络对小目标的识别能力;同时,为了防止额外参数的增加,将减少头网络中的一层卷积层并缩减其通道数。其次,提出一种通道-空间注意力机制模块(Channel Spatial Attention Module,CSAM),利用最优的权重分配使网络聚焦于特征图中小目标密集的区域。最后,提出一种带位置引导的标签分配策略(LB-SimOTA),根据每个预测框和真实框的交并比(IOU)的大小,分别配以不同的权重,以改善网络中整体预测框的质量。在小目标居多的数据集VisDrone2019上的实验结果表明,文中提出的算法和YOLOX-S相比,针对车和人的检测精度提升了8.63%,检测速度FPS也可达到86,因此更适合在无人机对地面小目标检测的场景下部署。