Aerial UAV Research Articles

Towards UAV Aerial Base Station Networking Robustness: A Jitter-Aware Antenna Selection Perspective

Towards UAV Aerial Base Station Networking Robustness: A Jitter-Aware Antenna Selection Perspective

AMFEF-DETR: An End-to-End Adaptive Multi-Scale Feature Extraction and Fusion Object Detection Network Based on UAV Aerial Images

AMFEF-DETR: An End-to-End Adaptive Multi-Scale Feature Extraction and Fusion Object Detection Network Based on UAV Aerial Images

Improved YOLOv7-Tiny for Object Detection Based on UAV Aerial Images

Improved YOLOv7-Tiny for Object Detection Based on UAV Aerial Images

Application of Orthophoto Maps Created from UAV Aerial Images for Monitoring Historical and Cultural Heritage Lands

The study investigates the methodology of creating orthophotoplans for immovable historical and cultural heritage sites, beginning from aerial surveys using various UAVs to object vectorization and plan creation. This issue is highly relevant in Ukraine due to rapid reforms in all ministries, including the Ministry of Culture. The reforms are creating a constant need for updated graphical documentation. As immovable historical and cultural heritage sites are in tourist areas with changing infrastructure, traditional surveying methods are impractical due to time constraints. Additionally, the application of UAV aerial imaging for delineating object boundaries and protective zones is more cost-effective. Existing boundaries of heritage lands and those determined from archival materials were overlaid on the created pflans. Orthophotoplans were produced and vectorized at a scale of 1:500 for two research zones: the Holy Trinity Church in Liuboml and the New Jewish Cemetery in Lviv. For the New Jewish Cemetery, boundaries were delineated to assess alignment with the current protective zone. Protective buffer zones around heritage sites were outlined according to both historical and contemporary recommendations in Ukraine. For buffer zones measured from the centroid, development is permitted up to 300 meters for undeveloped areas, with negotiated distances for developed regions, using the 300-meter protective zone as a baseline. The study's uniqueness lies in using a known methodology for creating orthophotoplans based on UAV data to explore options for modeling protective zones based on varying legislative norms over time, incorporating specific nuances. This approach can benefit not only Ukraine but also other countries undergoing reforms in this field.

ANALISIS POLA PERUBAHAN LAHAN SAWAH MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS (STUDI KASUS: KELURAHAN MAKROMAN, SAMARINDA)

Changes in rice fields are an important phenomenon that affects the sustainability of agriculture and the environment. This study aims to analyze the pattern of changes in rice fields in Makroman Village, using a Geographic Information System (GIS) approach. The method used is spatial analysis which involves such as spatial data creation, spatial data conversion, spatial data clustering, and mathematical calculation of spatial data. The spatial data used in this study include Ikonos Satellite Images of Makroman Village in 2011 and UAV Aerial Photos of Makroman Village in 2019. The results showed that the area of rice fields in Makroman Village increased from 96.196 ha to 129.343 ha in 2011-2019. Spatially, the increase occurred due to the presence of new rice fields and existing rice fields that did not decrease in number. Shrubs become land uses that have the potential to be converted in rice field extensification activities. GIS application is a method that can be used to answer the problems in this study to the level of detail, but some things that need to be considered in the application of this method are the quality of spatial data, especially remote sensing data used.

Lightweight Object Detection Networks for UAV Aerial Images Based on YOLO

Lightweight Object Detection Networks for UAV Aerial Images Based on YOLO

Coastal flood susceptibility assessment along the Northern coast of Portugal

Assessing the susceptibility of sandy beaches to flooding during major storms is an important topic, given the implications that these events can have for beach-dependent coastal systems. A flood susceptibility index based on wave run-up, storm surge and beach morphology is applied to three sectors of the northwest coast of Portugal. An extended ERA5 wave dataset is used (1950–2022) to characterize the most frequent storm events, and topographic data from aerial UAV surveys are examined to obtain specific morphological parameters of the sandy beaches – mean face slope and height of the beach. The flood susceptibility index results show that 54 % of the analyzed coast is classified with extreme or very high susceptibility. The most critical sector is the coastal stretch between Cortegaça and Torrão do Lameiro beaches, where mostly natural systems are present, while the urban front of Furadouro is frequently flooded during storm events. Between Miramar beach (south) and Paramos beach, with a susceptibility index classified between medium and extreme, the presence of some urban fronts points to high-risk exposure situations. The sector between São Jacinto and Poço da Cruz beaches presented lower susceptibility levels for the three urban areas. The problem does not end with the threat to urban fronts, which already have several defense structures, but it is also extends to dune systems and sand dykes.

Tracking deformation velocity via PSI and SBAS as a sign of landslide failure: an open-pit mine-induced landslide in Himmetoğlu (Bolu, NW Turkey)

A destructive landslide occurred in Himmetoğlu village in Göynük District (Bolu, NW Turkey) caused by open-pit coal mining activities. Field observations after the landslide failure and interviews with villagers motivated us to question the possibility of using satellite SAR data to detect precursory signs of failure with regard to deformation velocity. In this study, first, landslide deformations were mapped by applying the digital elevation model (DEM) of Difference (DoD) method using DEMs from aerial photography and UAV data. However, the primary aim was to track deformation velocity as a sign of landslide failure with persistent scatterers interferometry (PSI) and small baseline subset (SBAS) methods from Sentinel-1A data. For the SBAS, the deformation velocity for ascending and descending orbits varied between − 12 and 39 mm year−1 and between − 24 and 6 mm year−1, respectively. For the PSI, the deformation velocity for ascending and descending orbits varied between − 16 and 31 mm year−1 and between − 18 and 20 mm year−1, respectively. PSI and SBAS resulted in sharply changing line-of-sight displacement rates, which were interpreted as slope failure signs, from three months prior to the landslide. In addition, higher deformation velocities were observed in locations closer to landslide crack as expected. Based on our findings, we concluded that SAR interferometric time-series analysis have the makings of being used as a suitable approach in early discerning and avoiding potential slope failures in open-pit mining areas, when it is made carefully by observing the progress in mining activities by considering the other factors such as rainfall and earthquakes.

Improved YOLOv7 Target Detection Algorithm Based on UAV Aerial Photography

With the rapid development of remote sensing technology, remote sensing target detection faces many problems; for example, there is still no good solution for small targets with complex backgrounds and simple features. In response to the above, we have added dynamic snake convolution (DSC) to YOLOv7. In addition, SPPFCSPC is used instead of the original spatial pyramid pooling structure; the original loss function was replaced with the EIoU loss function. This study was evaluated on UAV image data (VisDrone2019), which were compared with mainstream algorithms, and the experiments showed that this algorithm has a good average accuracy. Compared to the original algorithm, the mAP0.5 of the present algorithm is improved by 4.3%. Experiments proved that this algorithm outperforms other algorithms.

YoloOW: A Spatial Scale Adaptive Real-Time Object Detection Neural Network for Open Water Search and Rescue From UAV Aerial Imagery

YoloOW: A Spatial Scale Adaptive Real-Time Object Detection Neural Network for Open Water Search and Rescue From UAV Aerial Imagery

Improved YOLOv5s Algorithm for Small Target Detection in UAV Aerial Photography

Improved YOLOv5s Algorithm for Small Target Detection in UAV Aerial Photography

DMFF-YOLO: YOLOv8 Based on Dynamic Multiscale Feature Fusion for Object Detection on UAV Aerial Photography

DMFF-YOLO: YOLOv8 Based on Dynamic Multiscale Feature Fusion for Object Detection on UAV Aerial Photography

A Cooperative Target Localization Method Based on UAV Aerial Images

A passive localization algorithm based on UAV aerial images and Angle of Arrival (AOA) is proposed to solve the target passive localization problem. In this paper, the images are captured using fixed-focus shooting. A target localization factor is defined to eliminate the effect of focal length and simplify calculations. To synchronize the positions of multiple UAVs, a dynamic navigation coordinate system is defined with the leader at its center. The target positioning factor is calculated based on image information and azimuth elements within the UAV photoelectric reconnaissance device. The covariance equation is used to derive AOA, which is then used to obtain the target coordinate value by solving the joint UAV swarm positional information. The accuracy of the positioning algorithm is verified by actual aerial images. Based on this, an error model is established, the calculation method of the co-localization PDOP is given, and the correctness of the error model is verified through the simulation of the Monte Carlo statistical method. At the end of the article, the trackless Kalman filter algorithm is designed to improve positioning accuracy, and the simulation analysis is performed on the stationary and moving states of the target. The experimental results show that the algorithm can significantly improve the target positioning accuracy and ensure stable tracking of the target.

Analysis of Work Effectiveness According to Application of 3D Reality Model Based on UAV Aerial Photos in Urban Development Projects

Analysis of Work Effectiveness According to Application of 3D Reality Model Based on UAV Aerial Photos in Urban Development Projects

Enhanced open biomass burning detection: The BranTNet approach using UAV aerial imagery and deep learning for environmental protection and health preservation

Open biomass burning (OBB) in agriculture presents a significant and well-documented challenge, posing severe consequences for both environmental and human health. OBB releases air pollutants that degrade air quality and contribute to climate change, leading to premature deaths in regions with high concentrations of open crop straw burning (OCSB) emissions. Although policies aimed at prohibiting OBB are in place, the efficacy of these regulations in mitigating OCSB emissions remains ambiguous. Consequently, early prevention and monitoring of open biomass combustion are imperative for environmental preservation. Traditional monitoring techniques, reliant on fixed-position cameras, are constrained by their location and monitoring intensity, making concealed fire recognition a complex problem. To address this limitation and monitor the human living environment more flexibly and accurately, we propose a new method to identify straw fires in UAV Aerial Image Using CNN Branch Reinforce Transformer which named BranTNet, enabling early detection and rapid response to crop straw fires. By integrating computer vision technology and deep learning algorithms, straw fires in UAV-acquired aerial survey images can be detected and categorized. In the realm of artificial intelligence algorithms, we skillfully merge convolution and attention mechanisms, harnessing the full potential of both methodologies. Moreover, we seamlessly incorporate transfer learning, skillfully unifying self-training convolution modules with pre-trained transformer modules. This strategic amalgamation not only minimizes time costs but also ensures optimal experimental outcomes. Regarding data, we meticulously collected a substantial number of authentic samples, ensuring the sufficiency of our experimental dataset. The experimental results demonstrate that our proposed method exhibits exceptional accuracy and robustness in detecting and identifying straw fires in UAV aerial survey images. Our approach outperforms the use of convolution or attention mechanisms alone. By integrating this approach with drone technology, we unlock the potential for developing more versatile and precise monitoring solutions, expanding the application of drones to diverse domains. This progress contributes significantly to the early detection and prevention of crop straw fires, fundamentally reducing environmental pollution, curbing carbon emissions, and advancing the cause of carbon neutrality. This innovative technique for monitoring and preventing OBB holds substantial promise in mitigating the adverse effects of OBB on the environment and human health.

Automated Parcel Loading-Unloading Mechanism Design for Delivery UAV

Unmanned Aerial Vehicle or UAV has evolved rapidly because of improved regulations and advancement in technology. During the COVID-19 pandemic, delivery UAVs were utilized more often than ever to deliver medical supplies, as well as parcels. Delivery UAVs work by collecting the goods and keeping it in a designated holder, which it then navigates through an area and dropping it off at the receiving point. The main factors that are considered for the design of a delivery UAV mechanism includes size and weight of parcels, security and the efficiency of loading and unloading parcels. The drop-box mechanism takes in the factors contributing to existing delivery UAV mechanisms and provides a solution that can overcome most of these concerns. The mechanism was designed through SOLIDWORKS software and 3D-printed. With the help of SOLIDWORKS, an analysis was performed, and it was concluded that the mechanism could handle a maximum of 2kg. Aside from that, the mechanism drops off parcels automatically and encourages contactless delivery.

Multi-Agent Learning-Based Optimal Task Offloading and UAV Trajectory Planning for AGIN-Power IoT

UAV-based air-ground integrated computing networks (AGIN) have gained significant traction in remote areas for the Power Internet of Things (PIoT). This paper considers an AGIN-PIoT, where computing tasks generated by ground PIoT devices are offloaded to aerial UAVs that perform edge computing. Jointly optimizing task offloading and UAV trajectory poses challenges such as many decision variables, information uncertainty, and long-term queue delay constraints. Due to the limited battery capacity of PIoT devices and UAVs, our objective is to minimize system energy consumption under long-term queue delay constraints by jointly optimizing task offloading, trajectory planning, and computing resource assignment. In light of Lyapunov optimization, we decompose the original challenging optimization problem into two sub-problems: (1) task offloading and UAV trajectory planning and (2) aerial edge resource allocation. Accordingly, we develop a multi-agent deep reinforcement learning-based algorithm called AGIN-MADDPG for the former to achieve the maximum accumulative reward and propose a greedy solution for the latter. Extensive experiments and numerical results demonstrate that our approach can avoid the problem of gradient vanishing and outperforms other benchmark methods in terms of power consumption, task backlog, queue delay, and system throughput.

Lightweight Object Detection Algorithm for UAV Aerial Imagery

Addressing the challenges of low detection precision and excessive parameter volume presented by the high resolution, significant scale variations, and complex backgrounds in UAV aerial imagery, this paper introduces MFP-YOLO, a lightweight detection algorithm based on YOLOv5s. Initially, a multipath inverse residual module is designed, and an attention mechanism is incorporated to manage the issues associated with significant scale variations and abundant interference from complex backgrounds. Then, parallel deconvolutional spatial pyramid pooling is employed to extract scale-specific information, enhancing multi-scale target detection. Furthermore, the Focal-EIoU loss function is utilized to augment the model’s focus on high-quality samples, consequently improving training stability and detection accuracy. Finally, a lightweight decoupled head replaces the original model’s detection head, accelerating network convergence speed and enhancing detection precision. Experimental results demonstrate that MFP-YOLO improved the mAP50 on the VisDrone 2019 validation and test sets by 12.9% and 8.0%, respectively, compared to the original YOLOv5s. At the same time, the model’s parameter volume and weight size were reduced by 79.2% and 73.7%, respectively, indicating that MFP-YOLO outperforms other mainstream algorithms in UAV aerial imagery detection tasks.

Real-Time Vehicle Detection from UAV Aerial Images Based on Improved YOLOv5

Aerial vehicle detection has significant applications in aerial surveillance and traffic control. The pictures captured by the UAV are characterized by many tiny objects and vehicles obscuring each other, significantly increasing the detection challenge. In the research of detecting vehicles in aerial images, there is a widespread problem of missed and false detections. Therefore, we customize a model based on YOLOv5 to be more suitable for detecting vehicles in aerial images. Firstly, we add one additional prediction head to detect smaller-scale objects. Furthermore, to keep the original features involved in the training process of the model, we introduce a Bidirectional Feature Pyramid Network (BiFPN) to fuse the feature information from various scales. Lastly, Soft-NMS (soft non-maximum suppression) is employed as a prediction frame filtering method, alleviating the missed detection due to the close alignment of vehicles. The experimental findings on the self-made dataset in this research indicate that compared with YOLOv5s, the mAP@0.5 and mAP@0.5:0.95 of YOLOv5-VTO increase by 3.7% and 4.7%, respectively, and the two indexes of accuracy and recall are also improved.

Cooperative UAV Resource Allocation and Task Offloading in Hierarchical Aerial Computing Systems: A MAPPO-Based Approach

This paper investigates a hierarchical aerial computing system, where both HAPs and UAVs provision computation services for ground devices (GDs). Different from the existing works which ignored UAV task offloading to HAPs and suffered long transmission delay between HAPs and GDs, in our system, UAVs are responsible for collecting the tasks generated by GDs. Considering limited resources and constrained coverage, UAVs need to cooperatively allocate their resources (including spectrum, caching, and computing) to GDs. After collecting GD tasks, UAVs are allowed to offload part of these tasks to the HAP, in order to minimize task processing delay and then better satisfy GD delay requirement. Our objective is to maximize the amount of computed tasks while satisfying tasks heterogeneous QoS requirements through the joint optimization of UAV resource allocation and task offloading. To this end, a joint optimization problem is first formulated as a partially observable Markov decision process (POMDP) under the constraints of available resources, UAV energy, and collision avoidance. Then, we design a multi-agent proximal policy optimization (MAPPO)-based algorithm to solve the optimization problem. By introducing the centralized training with decentralized execution framework, UAVs acting as agents can cooperatively make decisions on ground devices association, resource allocation, and task offloading according to their local observations. In addition, state normalization and action mask are also adopted to improve training efficiency. Experiment results verify the efficiency of the proposed algorithm and the system performance is also analyzed by the numerical results.