Unmanned Aerial Vehicle Monitoring Research Articles

Comprehensive review of unmanned aerial vehicle application to safety mining management

The increasing interest of users in unmanned aerial vehicle (UAV) technology has developed new fields of application for it. Presently, UAVs are working in so many areas and the mining industry has shown grown interest in the use of UAVs for routine operations in surface, underground mines, and mine waste dumps. In this paper, a thorough review has been performed on the application of UAVs in mine safety management. Systematic reviews were conducted to summarize the results of over 60 papers published during the 2010-2021 period. The study offers a review of the most common applications involving mine safety, the styles of flying control, and the UAV types exploited. The paper also contributes to the identification of prevalent data acquired by sensors and points out the most popular processing techniques of aerial imagery. The obtained results revealed that UAVs are used at mining sites for safety purposes including mine surface displacements, stability monitoring, working environment, coal fire identification, drill and blast, and mine waste dump operations. This research hopes to provide a technical reference, expanding the knowledge and recognition of UAV monitoring to manage mine safety, as well as an assessment of applications in mining activities.

UAV swarm communication reliability based on a comprehensive SINR model

Communication reliability is one of the most important factors in ensuring the safe operation of unmanned aerial vehicles (UAVs). The reliability of wireless communication for a UAV swarm is typically characterized by the signal-to-interference-and-noise ratio (SINR). However, previous work on UAV swarm communication reliability did not use a comprehensive SINR model. In this paper, we derived two novel closed-form approximations – lognormal and generalized beta prime (GBP) - of UAV swarm communication reliability based on a comprehensive SINR model. The model includes shadowing, multipath fading, the dependence of fading on external factors, which are the probability of line-of-sight (LoS) and the physical environment, as well as all possible interference within the system, which may be from the UAVs and the ground control station (GCS). For typical swarm heights between 60 – 300 m and up to 32 UAVs, comparisons of the approximate and simulated reliabilities for UAV swarms with a radius up to a critical value (which increases with the number and transmit power of the UAVs) show that only the lognormal approximation is accurate for all uplink and downlink communications, up to a critical horizontal distance from the GCS (which increases with height). Benchmarking of the lognormal approximation shows that neglecting either shadowing or multipath fading leads to high approximation errors. An example of how the lognormal approximation can be used to evaluate and maintain the communication reliability of a UAV swarm during deployment is given. Furthermore, it can be used to derive other SINR-dependent performance metrics, such as ergodic capacity and symbol error rate, which are useful in UAV network design and monitoring.

Utilizing unmanned aerial vehicle technology for environmental monitoring: Future trends, methods, and applications

AbstractThis paper provides an overview of the application of Unmanned Aerial Vehicles (UAVs) in environmental monitoring, with a focus on air pollution surveillance. Initially, the paper highlights the significance of UAV technology for real‐time air quality monitoring in developing countries and explores the characteristics of fixed‐wing and multirotor UAVs in monitoring air pollutants. It then delves into methods of measuring air pollutants using UAV platforms, including the three‐dimensional distribution of pollutants around roadsides, green belts, and street‐facing communities. The advantages of UAV measurements, such as lower costs, greater flexibility, and the ability to monitor in three dimensions, are emphasized. Finally, the paper discusses future trends in the field of environmental monitoring using UAVs, including technological advancements, evolving regulatory policies, and integration with other technologies like Artificial Intelligence, big data analytics, and 5G communication. These developments suggest an increasingly significant role for UAVs in environmental monitoring, enhancing efficiency, reducing costs, and contributing to public participation and environmental awareness. However, due to the delays in publication and review processes, the most recent studies may not have been included in the literature review, leading to a scenario where the review might not reflect the latest research trends.

Novel intelligent grazing strategy based on remote sensing, herd perception and UAVs monitoring

Intelligent grazing is a new livestock husbandry development mode based on obtaining the multi-source information, ecological balance is the goal. This paper focuses on intelligent grazing, reviews from grass remote sensing and aerial seeding, unmanned aerial vehicles (UAVs) monitoring and intelligent grazing robot, and summarizing the development of intelligent grazing elements at this stage, exploring the new development direction of automatic grazing management with grazing robot at this stage, and proposes a novel intelligent grazing strategy based on remote sensing, herd perception and UAVs monitoring. Furthermore, focusing on the intelligent grazing needs, the digital twin pasture is built in this paper mainly serves the technical verification and data acquisition based on machine vision, simulating the behavior of the herd during grazing.

Extracting Micro-Doppler Features from Multi-Rotor Unmanned Aerial Vehicles Using Time-Frequency Rotation Domain Concentration

This study addresses the growing concern over the impact of small unmanned aerial vehicles (UAVs), particularly rotor UAVs, on air traffic order and public safety. We propose a novel method for micro-Doppler feature extraction in multi-rotor UAVs within the time-frequency transform domain. Utilizing competitive learning particle swarm optimization (CLPSO), our approach divides population dynamics into three subgroups, each employing unique optimization mechanisms to enhance local search capabilities. This method overcomes limitations in traditional Particle Swarm Optimization (PSO) algorithms, specifically in achieving global optimal solutions. Our simulation and experimental results demonstrate the method’s efficiency and accuracy in extracting micro-Doppler features of rotary-wing UAVs. This advancement not only facilitates UAV detection and identification but also significantly contributes to the fields of UAV monitoring and airspace security.

Semantic Segmentation Network Slimming and Edge Deployment for Real-Time Forest Fire or Flood Monitoring Systems Using Unmanned Aerial Vehicles

In recent years, there has been a significant increase in the demand for unmanned aerial vehicle (UAV)-based monitoring systems to ensure proper emergency response during natural disasters such as wildfires, hurricanes, floods, and earthquakes. This paper proposes a real-time UAV monitoring system for responding to forest fires or floods. The proposed system consists of a hardware part and a software part. The hardware configuration is an embedded camera board mounted on the UAV, a Qualcomm QCS610 SoC with cores suitable for running deep learning-based algorithms. The software configuration is a deep learning-based semantic segmentation model for detecting fires or floods. To execute the model in real time on edge devices with limited resources, we used a network slimming technique which generates a lightweight model with reduced model size, number of parameters, and computational complexity. The performance of the proposed system was evaluated on the FLAME dataset consisting of forest fire images and the FloodNet dataset consisting of flood images. The experimental results showed that the mIoU of slimmed DeepLabV3+ for FLAME is 88.29%, and the inference speed is 10.92 fps. For FloodNet, the mIoU of the slimmed DeepLabV3+ is 94.15%, and the inference speed is 13.26 fps. These experimental results confirm that the proposed system is appropriate for accurate, low-power, real-time monitoring of forest fires and floods using UAVs.

FuF-Det: An Early Forest Fire Detection Method under Fog

In recent years, frequent forest fires have seriously threatened the earth’s ecosystem and people’s lives and safety. With the development of machine vision and unmanned aerial vehicle (UAVs) technology, UAV monitoring combined with machine vision has become an important development trend in forest fire monitoring. In the early stages, fire shows the characteristics of a small fire target and obvious smoke. However, the presence of fog interference in the forest will reduce the accuracy of fire point location and smoke identification. Therefore, an anchor-free target detection algorithm called FuF-Det based on an encoder–decoder structure is proposed to accurately detect early fire points obscured by fog. The residual efficient channel attention block (RECAB) is designed as a decoder unit to improve the problem of the loss of fire point characteristics under fog caused by upsampling. Moreover, the attention-based adaptive fusion residual module (AAFRM) is used to self-enhance the encoder features, so that the features retain more fire point location information. Finally, coordinate attention (CA) is introduced to the detection head to make the image features correspond to the position information, and improve the accuracy of the algorithm to locate the fire point. The experimental results show that compared with eight mainstream target detection algorithms, FuF-Det has higher average precision and recall as an early forest fire detection method in fog and provides a new solution for the application of machine vision to early forest fire detection.

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 Application of UAVs in the Evaluation of Thermal Comfort Levels in Buildings Equipped with Internal Greenhouses

A greenhouse is used to improve thermal comfort (TC) levels for its occupants in winter conditions using solar radiation, which involves low energy consumption. The aim of this research is the application of unmanned aerial vehicles (UAVs) in the evaluation of thermal comfort levels in buildings equipped with internal greenhouses. The new building design is developed numerically, and a building thermal simulator (BTS) numerical model calculates the indoor environmental variables. A new alternative and expeditious method to measure occupants’ comfort levels using UAV technology is applied using a UAV dynamic simulator (UAV DS). The evolution of the measured variables used for evaluating the predicted mean vote (PMV) is compared using the two numerical methodologies: BTS and UAV DS. In the second one, the mean radiant temperature (MRT) measuring methodology, the floor temperature, the lateral walls’ temperatures, the ceiling temperatures, and the air temperature are applied. In the method presented in this paper, a new building design is developed numerically, which includes a central greenhouse equipped with a semispherical dome, four auditoriums distributed around the central greenhouse, occupant distribution, and a ventilation methodology. The building geometry, the solar radiation on transparent surfaces, the TC, and the UAV mission methods are presented. The results show that, in general, the central greenhouse and the ventilation methodologies provide acceptable TC levels. The UAV monitoring mission, which includes two vehicles, provides good environmental variable replication, particularly when the environmental variables present greater variations. In the auditorium and greenhouse, the ceiling and lateral surface temperatures, respectively, can be used as an MRT approximation. The BTS numerical model is also important for developing buildings using renewable energy sources to improve the TC levels.

UAV Monitoring Topsoil Moisture in an Alpine Meadow on the Qinghai–Tibet Plateau

Soil moisture is one of the most critical soil components for sustained plant growth and grassland management. Unmanned aerial vehicles (UAVs) are gradually replacing manual labor in various aspects of grassland management. However, their potential for monitoring soil moisture in grasslands remains largely unexplored. High vegetation coverage and frequent rainfall in the Tibetan Plateau pose a challenge for personnel working in alpine meadows. To explore the potential of UAV technology for soil moisture detection in these areas, we conducted a rainfall reduction experiment in Maqu County, China to understand the relationships among soil moisture, vegetation coverage, and visible-light images captured using UAVs. The findings indicated a significant correlation between topsoil moisture and the brightness values in visible-light images acquired by UAVs (p < 0.0001). These results demonstrated that visible-light brightness, vegetation coverage, rainfall reduction, and aboveground biomass can be utilized for estimating the topsoil moisture using these images (y = −0.2676 × Brightness + 0.2808 × Vegetation coverage −0.1862 × Rainfall reduction + 0.1357 × Aboveground biomass + 37.77). The model validation worked well (E = 0.8291, RS = −3.58%, RMA = 10.38%, RMSE = 3.5878, Pearson’s r = 0.9631, PSI = 0.0125). This study further addresses the problem of topsoil moisture measurement in flat areas of mesoscale moist alpine meadows and is expected to facilitate the widespread adoption of UAV use in grassland ecology research.

Review and assessment of technical and legal challenges in application of unmanned aerial vehicles in monitoring and inspection of bridges

Abstract Intelligent transportation systems (ITSs) initiated a paradigm shift in the operation and management of transportation infrastructure by automating data collection, processing, and management. The drive toward using robotics to automate operational tasks in infrastructure management has gained significant momentum in recent years, with the introduction of unmanned aerial vehicles (UAVs), widely known as drones, being a notable milestone in this endeavor. UAVs reduced cost, time, and labor for tasks such as bridge monitoring while accelerating speed and precision such as structural scanning. Although not explicitly classified within the existing ITS categories, UAVs are becoming widely acknowledged as a valuable tool for improving the intelligent operation and management of transportation systems. Their potential to gather and transmit data in real time offers new opportunities for more accurate and timely decision-making as well as improved safety and efficiency within transportation infrastructure. Nevertheless, significant concerns remain regarding the use of UAVs, particularly in busy highways or densely populated metropolitan areas. Engineering and technical challenges, privacy and safety concerns, potential liabilities, and audio-visual disturbance for the public are among the issues that have impeded the widespread adoption and full integration of UAVs as complementary tools for ITS. This paper conducts a comprehensive review of the potential applications of UAVs for infrastructure and bridge structural health monitoring, while also evaluating the challenges associated with their utilization in this context. Furthermore, the paper discusses the ramifications of these challenges and emphasizes the areas that necessitate further attention and investigation by future research.

SF-YOLOv5: Improved YOLOv5 with swin transformer and fusion-concat method for multi-UAV detection

When dealing with complex trajectories, and the interference by the unmanned aerial vehicle (UAV) itself or other flying objects, the traditional detecting methods based on YOLOv5 network mainly focus on one UAV and difficult to detect the multi-UAV effectively. In order to improve the detection method, a novel algorithm combined with swin transformer blocks and a fusion-concat method based on YOLOv5 network, so called SF-YOLOv5, is proposed. Furthermore, by using the distance intersection over union and non-maximum suppression (DIoU-NMS) as post-processing method, the proposed network can remove redundant detection boxes and improve the efficiency of the multi-UAV detection. Experimental results verify the feasibility and effectiveness of the proposed network, and show that the mAP trained on the two datasets used in experiments has been improved by 2.5 and 4.11% respectively. The proposed network can detect multi-UAV while ensuring accuracy and speed, and can be effectively used in the field of UAV monitoring or other types of multi-object detection applications.

Investigation of Frequency-Domain-Based Vibration Signal Analysis for UAV Unbalance Fault Classification

The flying reliability of Unmanned Aerial Vehicles (UAVs) and flying robots, which directly determines the operational degree of safety, is becoming more important in recent intelligent decades. Reliability and a high level of safety are critical for autonomously controlled flying robots, especially in transportation and entertainment applications. Subsequently, monitoring UAV health is crucial and essential for system safety, cost savings, and excellent dependability. The development of numerous monitoring strategies has resulted from the requirement for a simple and accurate unbalance classification procedure. This paper provides an Unbalance Classification and Isolation (UCI) system for multirotor UAV propeller impairments. The technique is based on the processing of signal vectors from a vibration sensor positioned in the lines of the intersection of a modern-day drone's four propulsion units, which supply data for the Fast Fourier Transform (FFT) feature extraction. To identify and locate broken blades, characteristic fault signatures collected from vibration signals are employed and displayed in real-time on the programming platform. A noticeable maximum frequency shifting percentage value of 4.2% is acquired when deviating from a healthy state. The results reveal that identifying and isolating defective rotor states has high sensitivity and outperforms current studies in regard to unbalance classification of UAVs. The adopted technique is an efficient and low-cost solution that can be implemented in any multirotor UAV.

SanJeeVni: Secure UAV-Envisioned Massive Vaccine Distribution for COVID-19 Underlying 6G Network.

Recently, unmanned aerial vehicles (UAVs) are deployed in Novel Coronavirus Disease-2019 (COVID-19) vaccine distribution process. To address issues of fake vaccine distribution, real-time massive UAV monitoring and control at nodal centers (NCs), the authors propose SanJeeVni, a blockchain (BC)-assisted UAV vaccine distribution at the backdrop of sixth-generation (6G) enhanced ultra-reliable low latency communication (6G-eRLLC) communication. The scheme considers user registration, vaccine request, and distribution through a public Solana BC setup, which assures a scalable transaction rate. Based on vaccine requests at production setups, UAV swarms are triggered with vaccine delivery to NCs. An intelligent edge offloading scheme is proposed to support UAV coordinates and routing path setups. The scheme is compared against fifth-generation (5G) uRLLC communication. In the simulation, we achieve and 86% improvement in service latency, 12.2% energy reduction of UAV with 76.25% more UAV coverage in 6G-eRLLC, and a significant improvement of [Formula: see text]% in storage cost against the Ethereum network, which indicates the scheme efficacy in practical setups.

Multi-exposure electric power monitoring image fusion method without ghosting based on exposure fusion framework and color dissimilarity feature.

To solve the ghosting artifacts problem in dynamic scene multi-scale exposure fusion, an improved multi-exposure fusion method has been proposed without ghosting based on the exposure fusion framework and the color dissimilarity feature of this study. This fusion method can be further applied to power system monitoring and unmanned aerial vehicle monitoring. In this study, first, an improved exposure fusion framework based on the camera response model was applied to preprocess the input image sequence. Second, the initial weight map was estimated by multiplying four weight items. In removing the ghosting weight term, an improved color dissimilarity feature was used to detect the object motion features in dynamic scenes. Finally, the improved pyramid model as adopted to retain detailed information about the poor exposure areas. Experimental results indicated that the proposed method improves the performance of images in terms of sharpness, detail processing, and ghosting artifacts removal and is superior to the five existing multi-exposure image fusion (MEF) methods in quality evaluation.

Smart Visual Sensing for Overcrowding in COVID-19 Infected Cities Using Modified Deep Transfer Learning

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as sensing devices can be useful in detecting overcrowding events. Accordingly, in this article, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on deep transfer learning. According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions.

Change Detection Analysis Using Drone-Based Photogrammetry for Long-Term Archaeological Site Erosion Monitoring

This paper presents a rapid, cost-effective, and non-specialist approach to long-term archaeological site monitoring that is repeatable and affordable. Advances in unmanned aerial vehicle (UAV)-based photogrammetry allow for the creation of multi-temporal three-dimensional (3D) models that permit accurate and in-depth analysis and quantification of landscape change through time. This paper presents a case study of aerial-based photogrammetric datasets using UAVs (i.e., drones) to conduct change detection analysis for monitoring continued erosion threatening an Indigenous buffalo jump in Alberta, Canada. The results demonstrate which areas of the site experienced the most significant change over a period of four years. By bridging gaps between traditional field survey and satellite-scale observations of impacts to large archaeological site complexes, UAV monitoring programs may become increasingly important as anthropogenic climate change continues to threaten heritage sites in Canada.

A Real-Time Tracking Algorithm for Multi-Target UAV Based on Deep Learning

UAV technology is a basic technology aiming to help realize smart living and the construction of smart cities. Its vigorous development in recent years has also increased the presence of unmanned aerial vehicles (UAVs) in people’s lives, and it has been increasingly used in logistics, transportation, photography and other fields. However, the rise in the number of drones has also put pressure on city regulation. Using traditional methods to monitor small objects flying slowly at low altitudes would be costly and ineffective. This study proposed a real-time UAV tracking scheme that uses the 5G network to transmit UAV monitoring images to the cloud and adopted a machine learning algorithm to detect and track multiple targets. Aiming at the difficulties in UAV detection and tracking, we optimized the network structure of the target detector yolo4 (You Only Look Once V4) and improved the target tracker DeepSORT, adopting the detection-tracking mode. In order to verify the reliability of the algorithm, we built a data set containing 3200 pictures of four UAVs in different environments, conducted training and testing on the model, and achieved 94.35% tracking accuracy and 69FPS detection speed under the GPU environment. The model was then deployed on ZCU104 to prove the feasibility of the scheme.

Deep-Ensemble-Learning-Based GPS Spoofing Detection for Cellular-Connected UAVs

Unmanned aerial vehicles (UAVs) are an emerging technology in the 5G-and-beyond systems with the promise of assisting cellular communications and supporting IoT deployment in remote and density areas. Safe and secure navigation is essential for UAV remote and autonomous deployment. Indeed, the opensource simulator can use commercial software-defined radio tools to generate fake global positioning system (GPS) signals and spoof the UAV GPS receiver to calculate wrong locations, deviating from the planned trajectory. Fortunately, the existing mobile positioning system can provide additional navigation for cellular-connected UAVs and verify the UAV GPS locations for spoofing detection, but it needs at least three base stations (BSs) at the same time. In this article, we propose a novel deep-ensemble-learning-based, mobile-network-assisted UAV monitoring and tracking system for cellular-connected UAV spoofing detection. The proposed method uses path losses between BSs and UAVs communication to indicate the UAV trajectory deviation caused by GPS spoofing. To increase the detection accuracy, three statistics methods are adopted to remove environmental impacts on path losses. In addition, deep ensemble learning methods are deployed on the edge cloud servers and use the multilayer perceptron (MLP) neural networks to analyze path losses statistical features for making a final decision, which has no additional requirements and energy consumption on UAVs. The experimental results show the effectiveness of our method in detecting GPS spoofing, achieving above 97% accuracy rate under two BSs, while it can still achieve at least 83% accuracy under only one BS.

Lightweight network learning with Zero-Shot Neural Architecture Search for UAV images

Lightweight Network Architecture is essential for autonomous and intelligent monitoring of Unmanned Aerial Vehicles (UAVs), such as in object detection, image segmentation, and crowd counting applications. The state-of-the-art lightweight network learning based on Neural Architecture Search (NAS) usually costs enormous computation resources. Alternatively, low-performance embedded platforms and high-resolution drone images pose a challenge for lightweight network learning. To alleviate this problem, this paper proposes a new lightweight object detection model, called GhostShuffleNet (GSNet), for UAV images, which is built based on Zero-Shot Neural Architecture Search. This paper also introduces the new components which compose GSNet, namely GhostShuffle units (loosely based on ShuffleNetV2) and the backbone GSmodel-L. Firstly, a lightweight search space is constructed with the GhostShuffle (GS) units to reduce the parameters and floating-point operations (FLOPs). Secondly, the parameters, FLOPs, layers, and memory access cost (MAC) as constraints add to search strategy on a Zero-Shot Neural structure search algorithm, which then searches for an optimal network GSmodel-L. Finally, the optimal GSmodel-L is used as the backbone network and a Ghost-PAN feature fusion module and detection heads are added to complete the design of the lightweight object detection network (GSNet). Extensive experiments are conducted on the VisDrone2019 (14.92%mAP) dataset and the our UAV-OUC-DET (8.38%mAP) dataset demonstrating the efficiency and effectiveness of GSNet. The completed code is available at: https://github.com/yfq-yy/GSNet.