Lightweight Unmanned Aerial Vehicles Research Articles

Low-cost UAV Coordinated Carbon Observation Network: carbon dioxide measurement with multiple UAVs

Improving the mitigation of global greenhouse effects requires drastic reductions in carbon emissions. Effective carbon management at different spatial scales is therefore crucial to a more sustainable path of economic development. Regional and local carbon management currently rely on the traditional bottom-up method of emission reports, which are known for their opaque and complex procedures. Novel top-down carbon monitoring methods including space-based, aerial and ground-based observations are tantalising alternatives to more reliable data sources of carbon emissions as well as sinks. Unmanned Aerial Vehicles (UAVs) based carbon monitoring is an upcoming field because of its lower costs, higher spatial-temporal resolution and operability at a wider range of weather conditions compared to satellite observations. UAVs equipped with Non-dispersive Infrared (NDIR) carbon dioxide (CO2) sensors can measure in the air with faster response and can intelligently navigate themselves within areas of greater interest. With the help of lighter NDIR payloads and multiple UAVs flying in synchrony, larger cross sections or areas for maximising information acquisition of emission plumes can be covered. In this article, we first introduce the design of a miniaturised NDIR payload, which is compatible with a lightweight UAV to achieve a single system weighing less than 4kg. Then, we test our design via comparisons against another NDIR payload to determine its accuracy (-0.82ppm and -0.44ppm) and precision (0.12ppm and -0.03ppm) in measurements of two vertical concentration profiles. Finally, we deployed four UAVs on two major anthropogenic emission source monitoring campaigns with coordinated flight patterns. To fully understand the benefit of multi-UAV carbon monitoring, we attempted two flight patterns and computed power plant emission rates via the cross-sectional flux method. While all flights successfully captured the concentration enhancements downwind of the power plants, the single-sectional flight pattern measured an emission rate of 10ktCO2/day which is 44% less than that reported monthly and the multi-sectional flight pattern yielded relatively varied results (8.5ktCO2/day on average, which is 54% less) for the respective sections due to wind underestimations, plume section undersampling and wind fluctuations.

Omni-OTPE: Omnidirectional Optimal Real-Time Ground Target Position Estimation System for Moving Lightweight Unmanned Aerial Vehicle.

Ground target detection and positioning systems based on lightweight unmanned aerial vehicles (UAVs) are increasing in value for aerial reconnaissance and surveillance. However, the current method for estimating the target's position is limited by the field of view angle, rendering it challenging to fulfill the demands of a real-time omnidirectional reconnaissance operation. To address this issue, we propose an Omnidirectional Optimal Real-Time Ground Target Position Estimation System (Omni-OTPE) that utilizes a fisheye camera and LiDAR sensors. The object of interest is first identified in the fisheye image, and then, the image-based target position is obtained by solving using the fisheye projection model and the target center extraction algorithm based on the detected edge information. Next, the LiDAR's real-time point cloud data are filtered based on position-direction constraints using the image-based target position information. This step allows for the determination of point cloud clusters that are relevant to the characterization of the target's position information. Finally, the target positions obtained from the two methods are fused using an optimal Kalman fuser to obtain the optimal target position information. In order to evaluate the positioning accuracy, we designed a hardware and software setup, mounted on a lightweight UAV, and tested it in a real scenario. The experimental results validate that our method exhibits significant advantages over traditional methods and achieves a real-time high-performance ground target position estimation function.

A Modified High-Precision Imaging Algorithm Based on Imaging Plane Optimization with Minimum Entropy Applied to UAV SAR

The miniaturized and lightweight unmanned aerial vehicle synthetic aperture radar (UAV SAR) is gradually becoming a research hotspot. The motion errors of UAVs lead to a deviation from a straight flight path couple with an unknown elevation of the target area, which leads to a degradation in SAR image quality. To achieve high-precision SAR imaging results, external terrain elevation information should be utilized. However, such data are challenging to obtain and limited in accuracy. In response to this problem, a modified high-precision imaging algorithm based on imaging plane optimization with minimum entropy is proposed. The proposed algorithm makes good use of the nonlinear trajectory of the UAV, which is unfriendly to imaging. Then, the image entropy is taken into account as the evaluation metric to acquire an approximated optimization imaging plane. Finally, the BP imaging is performed on the optimization imaging plane. The proposed method does not rely on external terrain information. Instead, it makes full use of the non-linear trajectory of the UAV and autonomously generates the optimal imaging plane for different terrain areas. By doing so, it achieves high-precision imaging results. Simulations and actual measurements have validated the effectiveness and enhancement of the proposed method.

UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey

Unmanned aerial vehicles (UAVs) have gained significant popularity across various domains, but their proliferation also raises concerns about security, public safety, and privacy. Consequently, the detection and tracking of UAVs have become crucial. Among the UAV-monitoring technologies, those suitable for urban Internet-of-Things (IoT) environments primarily include radio frequency (RF), acoustic, and visual technologies. In this article, we provide a comprehensive review of passive UAV surveillance technologies, encompassing RF-based, acoustic-based, and vision-based methods for UAV detection, localization, and tracking. Our research reveals that certain lightweight UAV depth detection models have been effectively downsized for deployment on edge devices, facilitating the integration of edge computing and deep learning. In the city-wide anti-UAV, the integration of numerous urban infrastructure monitoring facilities presents a challenge in achieving a centralized computing center due to the large volume of data. To address this, calculations can be performed on edge devices, enabling faster UAV detection. Currently, there is a wide range of anti-UAV systems that have been deployed in both commercial and military sectors to address the challenges posed by UAVs. In this article, we provide an overview of the existing military and commercial anti-UAV systems. Furthermore, we propose several suggestions for developing general-purpose UAV-monitoring systems tailored for urban environments. These suggestions encompass considering the specific requirements of the application scenario, integrating detection and tracking mechanisms with appropriate countermeasures, designing for scalability and modularity, and leveraging advanced data analytics and machine learning techniques. To promote further research in the field of UAV-monitoring systems, we have compiled publicly available datasets comprising visual, acoustic, and radio frequency data. These datasets can be employed to evaluate the effectiveness of various UAV-monitoring techniques and algorithms. All of the datasets mentioned are linked in the text or in the references. Most of these datasets have been validated in multiple studies, and researchers can find more specific information in the corresponding papers or documents. By presenting this comprehensive overview and providing valuable insights, we aim to advance the development of UAV surveillance technologies, address the challenges posed by UAV proliferation, and foster innovation in the field of UAV monitoring and security.

The impact of oblique images and flight‐planning scenarios on the accuracy of UAV 3D mapping

AbstractThe developments in lightweight unmanned aerial vehicles (UAVs) and structure‐from‐motion (SfM)‐based software have opened a new era in 3D mapping which is notably cost‐effective and fast, though the photogrammetric blocks lead to systematic height error due to inaccurate camera calibration parameters particularly when the ground control points (GCPs) are few and unevenly distributed. The use of onboard Global Navigation Satellite System (GNSS) receivers (such as RTK‐ or PPK‐based devices which use the DGNSS technique) to obtain the accurate coordinates of camera perspective centres has reduced the need for ground surveys, nevertheless, the aforementioned systematic error was reported in the UAV photogrammetric blocks. In this research, three flight‐planning scenarios with oblique imagery in addition to the traditional nadir block were evaluated and processed with four different processing cases. Therefore, 16 various blocks with different overlaps, direct and indirect georeferencing approaches as well as flight‐planning scenarios were tested to examine and offer the best imaging network. The results denote that the combination of oblique images located on a circle in the centre of the block with the nadir block provides the best self‐calibration functionality and improves the final accuracy by 50% (from 0.163 to 0.085 m) for direct georeferenced blocks and by 40% (from 0.042 to 0.026 m) for indirect georeferenced blocks.

Synthesis and characterization of lightweight unmanned aerial vehicle composite building material for defense application

Synthesis and characterization of lightweight unmanned aerial vehicle composite building material for defense application

Research on Identity Authentication Scheme for UAV Communication Network

Unmanned aerial vehicles (UAV) play a vital role in many fields, such as agricultural planting, security patrol, emergency rescue, and so on. The development and implementation of these devices have become vital in terms of reachability and usability. Unfortunately, as drones become more widely used in various fields, they become more and more vulnerable to attacks and security threats, including, but not limited to, eavesdropping, man-in-the-middle attacks, and known session key attacks. In order to deal with these attacks and security threats and meet the needs of lightweight UAV communication, a secure and efficient authentication scheme is essential. To meet the security and lightweight requirements of an identity authentication scheme in a UAV communication network, this paper proposes an identity authentication scheme sdronelig based on an elliptic curve cryptosystem. The scheme realizes the mutual authentication and session key agreement configuration between the UAV and the ground station, and the authentication and key agreement between the UAVs can be realized with the help of the control station. The sdronelig authentication scheme is based on the ECDH key exchange protocol in the elliptic curve cryptography algorithm and adopts the MAC message authentication code technology and the method of pre-calculating part of the process. Under the premise of ensuring the security of the UAV communication network, the authentication efficiency is improved, the communication overhead and communication times are reduced, and the lightweight requirement of the UAV authentication scheme is met. Additionally, a formal verification tool is used to verify the security of the sdronelig scheme under the Dolev-Yao threat model, which is suitable for UAV networks. Finally, a detailed comparative study was conducted on security features, communication overhead, the number of communications, and computational overhead. The results show that the proposed sdronelig authentication scheme not only provides superior security features but also has better or comparable overhead compared to other existing authentication schemes.

A Study of Collaborative Trajectory Planning Method Based on Starling Swarm Bionic Algorithm for Multi-Unmanned Aerial Vehicle

This academic paper addresses the challenges associated with trajectory planning for affordable and light-weight Unmanned Aerial Vehicle (UAV) swarms, despite limited computing resources and extensive cooperation requirements. Specifically, an imitation-based starling cluster cooperative trajectory planning technique is proposed for a fixed-wing model of a six-degree-of-freedom UAV cluster. To achieve this, dynamic trajectory prediction of the rapid random search tree is utilized to generate a track solution adapted to the terrain environment. Additionally, the Dubins aircraft path solution is applied as it is suitable for executing input track commands by the UAV model. Computational simulations on different cluster sizes show the approach can maintain the cluster state while navigating diverse terrains, with the track solution complying with the UAV’s physical model properties.

A Lightweight UAV System: Utilizing IMU Data for Coarse Judgment of Loop Closure

Unmanned aerial vehicles (UAVs) can experience significant performance issues during flight due to heavy CPU load, affecting their flight capabilities, communication, and endurance. To address this issue, this paper presents a lightweight stereo-inertial state estimator for addressing the heavy CPU load issue of ORB-SLAM. It utilizes nonlinear optimization and features to incorporate inertial information throughout the Simultaneous Localization and Mapping (SLAM) pipeline. The first key innovation is a coarse-to-fine optimization method that targets the enhancement of tracking speed by efficiently addressing bias and noise in the IMU parameters. A novel visual–inertial pose graph is proposed as an observer to assess error thresholds and guide the system towards visual-only or visual–inertial maximum a posteriori (MAP) estimation accordingly. Furthermore, this paper introduces the incorporation of inertial data in the loop closure thread. The IMU data provide displacement direction relative to world coordinates, which is serving as a necessary condition for loop detection. The experimental results demonstrate that our method maintains excellent localization accuracy compared to other state-of-the-art approaches on benchmark datasets, while also significantly reducing CPU load.

Quantitative estimation of organ-scale phenotypic parameters of field crops through 3D modeling using extremely low altitude UAV images

Plant phenotypic parameters provide key information in modern crop breeding. However, the rapid and accurate estimation of organ-scale phenotypic parameters remains a challenge. In this context, the present study proposed a novel methodology for the automatic quantification of the organ-scale parameters of field crops using the unmanned aerial vehicle (UAV) platform. First, a lightweight UAV was employed to capture the multi-view and high-resolution image sequences of field crops at an extremely-low flight altitude. Subsequently, based on these image sequences, point cloud reconstruction of the canopy was conducted. Next, the geometrical model of individual leaves was reconstructed using the modified Crust algorithm and optimized by abnormal facet elimination and leaf surface repair. Finally, individual leaf phenotypic parameters were calculated based on the reconstructed geometrical models. The method was evaluated by comparing the calculated parameters with actual measurements. The calculated values for leaf length, maximum leaf width, and leaf area were in good agreements with the measured values (maize: R2 > 0.97 for all parameters, RMSE for length, width, and leaf area was 2.6 cm, 0.4 cm, and 33.2 cm2, respectively; soybean: R2 > 0.85 for all parameters, RMSE for the counterparts was 0.3 cm, 0.5 cm, and 2.3 cm2, respectively; tobacco: R2 > 0.89 for all parameters, RMSE for the counterparts was 4.1 cm, 1.4 cm, and 42.6 cm2, respectively). The methodology based on extremely-low altitude UAV images has promising prospects in the crop breeding program for the automatic acquisition of fine organ-scale parameters with high efficiency.

High-precision real-time UAV target recognition based on improved YOLOv4

In recent years, unmanned aerial vehicles (UAVs) have gained widespread use in both military and civilian fields with the advancement of aviation technology and improved communication capabilities. However, the phenomenon of unauthorized UAV flights, or “black flying”, poses a serious threat to the safe flight of aircraft in airspace and public safety. To effectively interfere with and attack UAV targets, it is crucial to enhance the detection and identification of “low, slow and small” UAVs. This study focuses on achieving high-precision and lightweight detection and identification of four-rotor, six-rotor, and fixed-wing UAVs in low-altitude complex environments. By combining deep learning target detection with superresolution feature enhancement, a lightweight UAV detection model is designed and field-tested for verification. To address the challenge of detecting small UAV targets with limited information, the feature fusion network is enhanced based on the traditional YOLOv4 algorithm to improve the detection ability of small targets via small target enhancement and candidate box adjustment. The feasibility of the improved network is quantitatively and qualitatively analyzed. Channel pruning and layer pruning are then applied to the network, significantly reducing its depth and width and realizing a lightweight network. Finally, reasoning quantification is conducted on the embedded platform to enable end-side deployment of the target detection algorithm.

Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends.

Recently, unmanned aerial vehicles (UAVs) or drones have emerged as a ubiquitous and integral part of our society. They appear in great diversity in a multiplicity of applications for economic, commercial, leisure, military and academic purposes. The drone industry has seen a sharp uptake in the last decade as a model to manufacture and deliver convergence, offering synergy by incorporating multiple technologies. It is due to technological trends and rapid advancements in control, miniaturization, and computerization, which culminate in secure, lightweight, robust, more-accessible and cost-efficient UAVs. UAVs support implicit particularities including access to disaster-stricken zones, swift mobility, airborne missions and payload features. Despite these appealing benefits, UAVs face limitations in operability due to several critical concerns in terms of flight autonomy, path planning, battery endurance, flight time and limited payload carrying capability, as intuitively it is not recommended to load heavy objects such as batteries. As a result, the primary goal of this research is to provide insights into the potentials of UAVs, as well as their characteristics and functionality issues. This study provides a comprehensive review of UAVs, types, swarms, classifications, charging methods and regulations. Moreover, application scenarios, potential challenges and security issues are also examined. Finally, future research directions are identified to further hone the research work. We believe these insights will serve as guidelines and motivations for relevant researchers.

A Lightweight Uav Swarm Detection Method Integrated Attention Mechanism

Aiming at the problems of low detection accuracy and large computing resource consumption of existing Unmanned Aerial Vehicle (UAV) detection algorithms for anti-UAV, this paper proposes a lightweight UAV swarm detection method based on You Only Look Once Version X (YOLOX). This method uses depthwise separable convolution to simplify and optimize the network, and greatly simplifies the total parameters, while the accuracy is only partially reduced. Meanwhile, a Squeeze-and-Extraction (SE) module is introduced into the backbone to improve the model′s ability to extract features; the introduction of a Convolutional Block Attention Module (CBAM) in the feature fusion network makes the network pay more attention to important features and suppress unnecessary features. Furthermore, Distance-IoU (DIoU) is used to replace Intersection over Union (IoU) to calculate the regression loss for model optimization, and data augmentation technology is used to expand the dataset to achieve a better detection effect. The experimental results show that the mean Average Precision (mAP) of the proposed method reaches 82.32%, approximately 2% higher than the baseline model, while the number of parameters is only about 1/10th of that of YOLOX-S, with the size of 3.85 MB. The proposed approach is, thus, a lightweight model with high detection accuracy and suitable for various edge computing devices.

An automated, high-performance approach for detecting and characterizing broccoli based on UAV remote-sensing and transformers: A case study from Haining, China

• A lightweight UAV platform for detecting and characterizing broccoli was developed. • Transformers was chosen because of its success in natural language processing. • Existing TransUet and PCT provide a good balance between the accuracy and speed. • Our integrated framework is robust against variations of environmental conditions. Accurate canopy mapping and head-volume estimation of large areas of broccoli is an important prerequisite for precision farming since it provides important phenotypic traits associated with field management, environmental control, and yield prediction. Currently, the detection and characterization of broccoli mostly rely on ground surveys and human interpretation, which is often time- and labor-intensive. Recent developments based on unmanned aerial vehicle (UAV) remote sensing offer low cost, timely, and flexible data acquisition, thereby providing a potential alternative technique to enhance in situ field surveys. The combination of UAV data and deep learning has led to a series of breakthroughs in rapid and automated collection of simultaneous multisensor and multimodal plant phenotyping data. However, their application for monitoring broccoli remains problematic when faced with the significant spatial scale involved and the variety of vegetation species. To address this problem, we propose herein a fast and reliable semi-automatic workflow based on deep learning to process UAV RGB imagery and LiDAR point clouds and thereby remotely detect and characterize broccoli canopy and heads. First, we explore the use of TransUNet to differentiate canopy and non-canopy regions in RGB images at the individual-plant scale. The results demonstrate that TransUNet consistently achieves the highest accuracy (average returned Precision, Recall, F1 score, and IoU of 0.917, 0.864, 0.901, and 0.895, respectively) compared with three CNN-based and two shallow learning-based approaches. In addition, TransUNet performs best in terms of robustness against variations in training samples. Subsequently, to estimate the volume of broccoli heads, a point cloud transformer (PCT) network is developed for point cloud segmentation. Improving upon the results of three existing methods PointNet, PointNet++, and K-means that were applied to the same datasets, the best-performing PCT produced a precision of 0.914, an overall recall of 0.899, an overall F1 score of 0.901, and an overall IoU of 0.879. A regression analysis indicates that the PCT estimates had R 2 = 0.875, RMSE = 18.62, and rRMSE = 3.64 %, which is also superior to the results from other comparison approaches. Collectively, the wide application of such technology would facilitate applied research in plant phenotyping and precision agro-ecological applications and field management.

A Free-Rotation Asymmetric Magnetic Coupling Structure of UAV Wireless Charging Platform With Conformal Pickup

This article proposes an asymmetric magnetic coupler using the horizontal magnetic field to realize the conformal and lightweight unmanned aerial vehicle (UAV) side pickup mechanism and solve the problems of rotation misalignment and horizontal offset of UAVs. The novel structure uses multiple groups of series circular coils as the transmitting coil to construct a uniform near harness horizontal magnetic field. The solenoid structure covered with flexible magnetic material is used as the receiver, which is attached to the UAV landing gear. A 200-W prototype is produced to validate the proposed structure. The system can maintain smooth mutual inductance with a dc–dc efficiency of 90% under a ±20 mm offset and free-rotation.

A Lightweight and Accurate UAV Detection Method Based on YOLOv4.

At present, the UAV (Unmanned Aerial Vehicle) has been widely used both in civilian and military fields. Most of the current object detection algorithms used to detect UAVs require more parameters, and it is difficult to achieve real-time performance. In order to solve this problem while ensuring a high accuracy rate, we further lighten the model and reduce the number of parameters of the model. This paper proposes an accurate and lightweight UAV detection model based on YOLOv4. To verify the effectiveness of this model, we made a UAV dataset, which contains four types of UAVs and 20,365 images. Through comparative experiments and optimization of existing deep learning and object detection algorithms, we found a lightweight model to achieve an efficient and accurate rapid detection of UAVs. First, from the comparison of the one-stage method and the two-stage method, it is concluded that the one-stage method has better real-time performance and considerable accuracy in detecting UAVs. Then, we further compared the one-stage methods. In particular, for YOLOv4, we replaced MobileNet with its backbone network, modified the feature extraction network, and replaced standard convolution with depth-wise separable convolution, which greatly reduced the parameters and realized 82 FPS and 93.52% mAP while ensuring high accuracy and taking into account the real-time performance.

Lightweight unmanned aerial vehicle video object detection based on spatial‐temporal correlation

SummaryIntelligent unmanned aerial vehicles (UAVs) are drawing more and more attention from industry to academia. UAV navigation plays an important role in the cooperative scenario where multiple UAVs are deployed, while image data that capture the information of the UAV area are often used as input for UAV navigation. Deep learning is a common and powerful technique for UAV image processing, but a complex model generated by deep learning technique is hardly suitable for the limited computing capacity of edge computing devices such as UAVs. Therefore, this paper designs an efficient deep learning model on UAVs to fit the restriction of low computational powers and low power consumption. Traditional UAV object detection methods mostly use static images as the basis for object recognition, or collect images for offline detection. Our method combines the existing fast single‐frame detection methods with the spatial‐temporal relationship of video sequences, to build an efficient end‐to‐end model. In addition, the convolutional LSTM module is used to propagate the temporal context of the video frame sequences. Based on the temporal context, we propose a module for calculating spatial correlation. At the same time, we establish our experimental dataset in our real application and conduct the experiment, which shows that the proposed method reduces the size of models and meanwhile maintains the detection rate. Compared with the existing static images approaches, our method is faster and more accurate. Inference speeds of nearly 20fps can be achieved while performing real‐time tasks.

Diurnal variations in directional brightness temperature over urban areas through a multi-angle UAV experiment

Quasi-synchronous multi-angle observations of urban surface temperature (UST) are critical for understanding the urban thermal anisotropy (UTA) regime and validating UTA models. Such observations can be performed by thermal sensors onboard lightweight unmanned aerial vehicles (UAVs), due to their flexibility in sampling from different directions. This study designed a straightforward and efficient protocol to obtain quasi-synchronous multi-angle USTs based on a lightweight UAV for a typical residential area in Nanjing (China). By using this protocol, we retrieved a directional brightness temperature (DBT) dataset with 73 DBTs 12 times throughout the diurnal cycle. Our results exhibited that the diurnal UTA intensity (UTAI) well corresponds to that of air temperature: the maximum UTAI (14.0 °C) occurred around 14:00 to 15:00 local time, and the minimum (3.5 °C) occurred at night. Interestingly, the nighttime DBT variations were found to depend both on viewing zenith and azimuth angles, and a slight hotspot effect was identified. These findings notably differ from previous studies based on modeling, which often assumed the independence of the nighttime DBT of the viewing azimuth angle and the absence of the hotspot effect. The analogous pattern of UTA between the daytime and nighttime (especially before midnight) was seemingly driven by a remarkable temperature contrast among urban surface components that can persist for many hours after sunset. Thus, the designed observation protocol can be applied to retrieve quasi-synchronous multi-angle USTs for other types of urban surfaces, thereby facilitating a complete understanding of diurnal UTA variations and assisting the validations of UTA models.

Comparison of Deep Learning Methods for Detecting and Counting Sorghum Heads in UAV Imagery

With the rapid development of remote sensing with small, lightweight unmanned aerial vehicles (UAV), efficient and accurate crop spike counting, and yield estimation methods based on deep learning (DL) methods have begun to emerge, greatly reducing labor costs and enabling fast and accurate counting of sorghum spikes. However, there has not been a systematic, comprehensive evaluation of their applicability in cereal crop spike identification in UAV images, especially in sorghum head counting. To this end, this paper conducts a comparative study of the performance of three common DL algorithms, EfficientDet, Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLOv4), for sorghum head detection based on lightweight UAV remote sensing data. The paper explores the effects of overlap ratio, confidence, and intersection over union (IoU) parameters, using the evaluation metrics of precision P, recall R, average precision AP, F1 score, computational efficiency, and the number of detected positive/negative samples (Objects detected consistent/inconsistent with real samples). The experiment results show the following. (1) The detection results of the three methods under dense coverage conditions were better than those under medium and sparse conditions. YOLOv4 had the most accurate detection under different coverage conditions; on the contrary, EfficientDet was the worst. While SSD obtained better detection results under dense conditions, the number of over-detections was larger. (2) It was concluded that although EfficientDet had a good positive sample detection rate, it detected the fewest samples, had the smallest R and F1, and its actual precision was poor, while its training time, although medium, had the lowest detection efficiency, and the detection time per image was 2.82-times that of SSD. SSD had medium values for P, AP, and the number of detected samples, but had the highest training and detection efficiency. YOLOv4 detected the largest number of positive samples, and its values for R, AP, and F1 were the highest among the three methods. Although the training time was the slowest, the detection efficiency was better than EfficientDet. (3) With an increase in the overlap ratios, both positive and negative samples tended to increase, and when the threshold value was 0.3, all three methods had better detection results. With an increase in the confidence value, the number of positive and negative samples significantly decreased, and when the threshold value was 0.3, it balanced the numbers for sample detection and detection accuracy. An increase in IoU was accompanied by a gradual decrease in the number of positive samples and a gradual increase in the number of negative samples. When the threshold value was 0.3, better detection was achieved. The research findings can provide a methodological basis for accurately detecting and counting sorghum heads using UAV.

Toward flexible and persistent UAV service: Multi-period and multi-objective system design with task assignment for disaster management

Usability of lightweight unmanned aerial vehicles (UAVs) limited in a variety of activities in disaster management due to two fundamental drawbacks: limited flight duration, and limited loading capacity. With the proper system design and task assignment methodologies, these limitations can be overcome to enable long-duration UAV missions over large areas. The present study set out to determine the optimal unmanned aerial system (UAS) system design and UAV task assignment for such applications. Based on a proposed multi-objective mathematical model, exact Pareto optimal solutions were derived using the ε–constraint method. To find approximate Pareto solutions, an approximate two-phase (ATP) approach was developed, which is computationally inexpensive compare to the optimal solution approach. Through numerical experiments based on a realistic case study, it was shown that the ATP approach results in high accuracy and a two-orders-of-magnitude reduction in computation time. Additionally, for prompt application of our study to continuously changing situations, a rolling-horizon UAS approach herein is suggested.