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.

High Spatial Resolution Mapping of Dykes Using Unmanned Aerial Vehicle(UAV) Photogrammetry: New Insights On Emplacement Processes

Remote sensing has played a pivotal role in our understanding of the geometry of dykes and dyke swarms on Earth,Venus and Mars(West and Ernst,1991;Mege and Masson,1995;Ernst et al.,2005).Since the 1970’s

Simulating unmanned aerial vehicle flight control and collision detection

An unmanned aerial vehicle(UAV)is a small,fast aircraft with many useful features.It is widely used in military reconnaissance,aerial photography,searches,and other fields;it also has very good practical-application and development prospects.Since the UAV’s flight orientation is easily changeable,its orientation and flight path are difficult to control,leading to its high damage rate.Therefore,UAV flight-control technology has become the focus of attention.This study focuses on simulating a UAV’s flight and orientation control,and detecting collisions between a UAV and objects in a complex virtual environment.The proportional-integral-derivative control algorithm is used to control the orientation and position of the UAV in a virtual environment.A version of the bounding-box method that combines a grid with a k-dimensional tree is adopted in this paper,to improve the system performance and accelerate the collision-detection process.This provides a practical method for future studies on UAV flight position and orientation control,collision detection,etc.

Rice Bacterial Infection Detection Using Ensemble Technique on Unmanned Aerial Vehicles Images

Establishing a system for measuring plant health and bacterial infection is critical in agriculture.Previously,the farmers themselves,who observed them with their eyes and relied on their experience in analysis,which could have been incorrect.Plant inspection can determine which plants reflect the quantity of green light and near-infrared using infrared light,both visible and eye using a drone.The goal of this study was to create algorithms for assessing bacterial infections in rice using images from unmanned aerial vehicles(UAVs)with an ensemble classification technique.Convolution neural networks in unmanned aerial vehi-cles image were used.To convey this interest,the rice’s health and bacterial infec-tion inside the photo were detected.The project entailed using pictures to identify bacterial illnesses in rice.The shape and distinct characteristics of each infection were observed.Rice symptoms were defined using machine learning and image processing techniques.Two steps of a convolution neural network based on an image from a UAV were used in this study to determine whether this area will be affected by bacteria.The proposed algorithms can be utilized to classify the types of rice deceases with an accuracy rate of 89.84 percent.

Unmanned Aerial Vehicle Control of Major Sugarcane Diseases and Pests in Low Latitude Plateau

This research was aimed at the defects in traditional artificial spraying control method and the problems such as the difficulty in pesticides applying,labor shortage and low operating efficiency in the middle and late stage of sugarcane high stalk crops.The aerial pesticide application technology for sugarcane main diseases and pests was systematically developed and demonstrated from the aspects of aircraft type choice,selection of special pesticides and auxiliaries,integration of pesticides and equipment,field operation,technical specifications,and large-scale application organization mode.The UAV model and flight technical parameters suitable for the sugarcane planting area in low-latitude plateau were analyzed,and the optimal agent formulation combination and application technology of the UAV flight control were screened out,and the UAV flight control was applied to the major sugarcane pests and diseases control in the low-latitude plateau in large scale(UAV flight control was popularized and applied to 15 527 hm 2 in 2018).The research results provided mature whole-process technical support for the normalization of the application of the UVA flight control of major sugarcane pests and diseases.The UAV control technology for major sugarcane pests and diseases had the advantages of ultra-low pesticides applying dosage and high operating efficiency,and could effectively solve the problems such as the difficulty in pesticides applying,labor shortage and low operating efficiency in the middle late growth stage of high stalk crops.This technology successfully opened up a simple,efficient and new way for the effective control of major sugarcane pests and diseases,and practically accelerated the process of integrated control and prevention of sugarcane pests and diseases.In addition,this technology had an extremely significant effect on reducing the loss of sugarcane farmers and enterprises caused by the epidemic and outbreak of sugarcane pests and diseases,increasing sugarcane yield and sugar content.At the same time,this technology played an important role in realizing the whole-process precise control of sugarcane pests and diseases,improving the quality and increasing the efficiency of sugarcane,and guaranteeing the national sugar safety.

Unmanned aerial vehicle based intelligent triage system in mass-casualty incidents using 5G and artificial intelligence

BACKGROUND:Rapid on-site triage is critical after mass-casualty incidents(MCIs)and other mass injury events.Unmanned aerial vehicles(UAVs)have been used in MCIs to search and rescue wounded individuals,but they mainly depend on the UAV operator’s experience.We used UAVs and artificial intelligence(AI)to provide a new technique for the triage of MCIs and more efficient solutions for emergency rescue.METHODS:This was a preliminary experimental study.We developed an intelligent triage system based on two AI algorithms,namely OpenPose and YOLO.Volunteers were recruited to simulate the MCI scene and triage,combined with UAV and Fifth Generation(5G)Mobile Communication Technology real-time transmission technique,to achieve triage in the simulated MCI scene.RESULTS:Seven postures were designed and recognized to achieve brief but meaningful triage in MCIs.Eight volunteers participated in the MCI simulation scenario.The results of simulation scenarios showed that the proposed method was feasible in tasks of triage for MCIs.CONCLUSION:The proposed technique may provide an alternative technique for the triage of MCIs and is an innovative method in emergency rescue.

Research on Vegetation Coverage Status of Urban-rural Fringe in Guizhou Using Unmanned Aerial Vehicle

Unmanned aerial vehicle technology was used to survey the vegetation coverage of typical urban-rural fringe, and descriptive statistics and geostatistical methods were used to analyze the urban-rural fringe of spatial heterogeneity of vegetation coverage. The results showed that vegetation coverage in the study area was 27.2176% with the coefficient of variation of 31.7786%; that the vegetation coverage in separation distance of 〈0.18＇ showed positive spatial correlation, and the spatial correlation of vegetation coverage in separation distance of 〈0.18＇ was greater than that in 〉0.18＇; that the best fitting model for Semivariance function was exponential model with spatial variation ratio 0.726, which showed strong spatial correlation, and the spatial correlated scale was 0.18＇; that the vegetation coverage data in the study area was relatively stable, and the instability mainly occurred on the border of the study area and the surroundings.

Autonomous Unmanned Aerial Vehicles Based Decision Support System for Weed Management

Recently,autonomous systems become a hot research topic among industrialists and academicians due to their applicability in different domains such as healthcare,agriculture,industrial automation,etc.Among the interesting applications of autonomous systems,their applicability in agricultural sector becomes significant.Autonomous unmanned aerial vehicles(UAVs)can be used for suitable site-specific weed management(SSWM)to improve crop productivity.In spite of substantial advancements in UAV based data collection systems,automated weed detection still remains a tedious task owing to the high resemblance of weeds to the crops.The recently developed deep learning(DL)models have exhibited effective performance in several data classification problems.In this aspect,this paper focuses on the design of autonomous UAVs with decision support system for weed management(AUAV-DSSWM)technique.The proposed AUAV-DSSWM technique intends to identify the weeds by the use of UAV images acquired from the target area.Besides,the AUAV-DSSWM technique primarily performs image acquisition and image pre-processing stages.Moreover,the Adam optimizer with You Only Look Once Object Detector-(YOLOv3)model is applied for the detection of weeds.For the effective classification of weeds and crops,the poor and rich optimization(PRO)algorithm with softmax layer is applied.The design of Adam optimizer and PRO algorithm for the parameter tuning process results in enhanced weed detection performance.A wide range of simulations take place on UAV images and the experimental results exhibit the promising performance of the AUAV-DSSWM technique over the other recent techniques with the accy of 99.23%.

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.

Safety Helmet Wearing Detection in Aerial Images Using Improved YOLOv4

In construction,it is important to check whether workers wear safety helmets in real time.We proposed using an unmanned aerial vehicle(UAV)to monitor construction workers in real time.As the small target of aerial photography poses challenges to safety-helmet-wearing detection,we proposed an improved YOLOv4 model to detect the helmet-wearing condition in aerial photography:(1)By increasing the dimension of the effective feature layer of the backbone network,the model’s receptive field is reduced,and the utilization rate of fine-grained features is improved.(2)By introducing the cross stage partial(CSP)structure into path aggregation network(PANet),the calculation amount of themodel is reduced,and the aggregation efficiency of effective features at different scales is improved.(3)The complexity of the YOLOv4 model is reduced by introducing group convolution and the pruning PANet multi-scale detection mode for de-redundancy.Experimental results show that the improved YOLOv4 model achieved the highest performance in the UAV helmet detection task,that the mean average precision(mAP)increased from83.67%of the original YOLOv4 model to 91.03%,and that the parameter amount of the model is reduced by 24.7%.The results prove that the improved YOLOv4 model can effectively respond to the requirements of real-time detection of helmet wearing by UAV aerial photography.

Road boundary estimation to improve vehicle detection and tracking in UAV video

Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do not work well for low volume road, which is not well-marked and with noises such as vehicle tracks. A fusion-based method termed Dempster-Shafer-based road detection(DSRD) is proposed to address this issue. This method detects road boundary by combining multiple information sources using Dempster-Shafer theory(DST). In order to test the performance of the proposed method, two field experiments were conducted, one of which was on a highway partially covered by snow and another was on a dense traffic highway. The results show that DSRD is robust and accurate, whose detection rates are 100% and 99.8% compared with manual detection results. Then, DSRD is adopted to improve UAV video processing algorithm, and the vehicle detection and tracking rate are improved by 2.7% and 5.5%,respectively. Also, the computation time has decreased by 5% and 8.3% for two experiments, respectively.

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

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.

A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable attention.One encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image classification.As a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent beneficiaries.This paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and vegetables.By meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use.Additionally,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context.The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture.Nevertheless,we also shed light on the various issues and limitations of working with spectral images.This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme

Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network(TDKD-Net) is proposed,where the TT-format TD(tensor decomposition) and equalweighted response-based KD(knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU(intersection of union) loss with optimal transport assignment(F-EIoU-OTA)mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation.

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).

改进YOLOv8的无人机航拍图像目标检测算法

针对无人机航拍图像存在多个小目标聚集、目标尺度变化大的问题,提出一种改进YOLOv8的目标检测算法TS-YOLO(tiny and scale-YOLO)。在主干部分去除冗余的特征提取层,设计了一种高效特征提取模块(efficient feature extraction module,EFEM),避免小目标特征消失在冗余信息中。在颈部设计了一种双重跨尺度加权特征融合方法(dual cross-scale weighted feature-fusion,DCWF),融合多尺度信息的同时抑制噪声干扰,提升特征表达能力。通过构建一种参数共享检测头(parameter-shared detection header,PSDH),使回归和分类任务实现参数共享,保证检测精度的同时有效降低了模型的参数量。所提模型在VisDrone-2019数据集上的精度(P)和召回率(R)分别达到54.0%、42.5%;相比于原始YOLOv8s模型,mAP50提高了5.0个百分点,达到44.5%,且参数量减少了55.8%,仅有4.94×106;在DOTAv1.0遥感数据集上,mAP50达到71.9%,仍具有较好的泛化能力。

面向应用场景的前沿技术识别方法

[研究目的]场景驱动创新是引领产业结构升级的关键路径,如何基于不同场景识别前沿技术成为推动新质生产力发展的重要议题。[研究方法]提出了一种面向应用场景的前沿技术识别方法。首先以专利、论文为数据源,基于前瞻性、先导性、探索性、颠覆性指标体系初步筛选前沿技术主题;其次以科技报告为数据源,基于技术-关系-技术(Technology-Relationship-Technology,TRT)结构的语义分析,分析技术应用场景;最后,构建专利、论文数据与科技报告的相似性矩阵,建立技术与场景识别结果的关联,据此进一步筛选前沿技术主题,并结合专家知识,识别出面向应用场景的前沿技术主题,提供了场景驱动实践探索的新方法。[研究结果/结论]以无人机目标识别与跟踪领域为例进行实证研究,在面向情报、监视与侦察(Intelligence,Surveillance,and Reconnaissance,ISR)、物资运输、医疗补给等14个应用场景的前沿技术识别结果显示,目标识别与跟踪算法设计、视觉系统与图像处理技术、任务规划技术、无线通信技术4项技术为该领域的前沿技术,验证了该识别方法的可行性与有效性。

无人机遥感技术在生态安全监测与评价中的应用研究

近年来,为了维持生态系统服务的可持续性,无人机遥感技术作为一种新兴的监测手段,在生态安全监测与评价领域得到了广泛关注和应用。探讨无人机遥感技术在生态安全监测与评价中的应用,分析其在数据采集、信息处理、监测评估等方面的优势和挑战,有助于行业人员和相关领域研究者更深入地理解该技术的应用潜力和发展方向。

不同针阔叶树蒸腾耗水特性及单位面积耗水量估算

为探究针阔叶树种单木蒸腾耗水规律,以河北省雄安新区主要造林树种针叶树油松和阔叶树楸树作为研究对象,运用热扩散式探针法(TDP)对油松(Pinus tabuliformis)、楸树(Catalpa bungei)的树干液流速率进行监测,并调查样木的冠幅、胸径、冠高和冠幅投影面积等林木因子,分析林木耗水量与林木因子的相关性,将实测的林木因子与耗水量拟合,建立不同林木因子与林木耗水量方程。结果表明:(1)不同冠幅投影面积的油松和楸树的液流速率及耗水日变化过程均呈昼高夜低的单峰型曲线;两个树种整体表现为冠幅投影面积越大,液流速率和耗水量越高,楸树的时均耗水量和日累计耗水量均高于油松。(2)两个树种的耗水量均与冠幅投影面积、冠幅、胸径呈极显著正相关关系(P<0.01);冠幅投影面积与日累计耗水量具有较高的相关性与较好的拟合效果,两树种的日累计耗水与冠幅投影面积的相关系数分别为0.773、0.901,确定系数(R~2)分别为0.596、0.811;油松和楸树平均单位冠幅投影面积日累计耗水量分别为0.290 6、0.302 3 kg·m^(-2)·d^(-1)。