Drones in Forest Fire Mitigation DOI

Vaishnavi Y Nayak,

Vaishnavi G Rao,

H Jagruthi

et al.

Published: July 19, 2023

The proposed system in this paper utilizes drones and Convolutional Neural Networks (CNN) for fire detection. Traditional smoke sensors can be slow cost-inefficient, making them less suitable early authors analyze the scope of CNN related methodologies detecting propose a novel that uses optical cameras mounted on to detect identify forest threats real-time. also aims notify interested parties authorities by providing alerts important information such as specific location environmental conditions. use equipped with is an innovative approach ability capture images transmit real-time enables detection identification fires they occur. allows efficient accurate analysis captured images, resulting reliable system. Additionally, send parties, allowing timely appropriate action taken. Overall, has potential revolutionize response. modern technology greatly improve efficiency accuracy detection, ultimately leading safer more secure environment.

Language: Английский

State-of-the-Art and Future Research Challenges in UAV Swarms DOI
Sadaf Javed, Ali Hassan, Rizwan Ahmad

et al.

IEEE Internet of Things Journal, Journal Year: 2024, Volume and Issue: 11(11), P. 19023 - 19045

Published: Feb. 9, 2024

Due to their potential accomplish complicated missions more effectively, UAV swarms have attracted a lot of attention lately. swarm offers enhanced intelligence, improved coordination, increased flexibility, survivability, and reconfigurability. It is multi-disciplinary system that necessitates tight integration several sub-systems, including optimal trajectory planning, localization, task etc. This review covers the important aspects formation control, communication, path autonomy, security. additionally explores recent technical advancements in algorithms made development complex systems possible. paper also provides insight into ethical use cases various military, civilian, entertainment applications. concluded by highlighting future directions challenges technology need for research exploit fully. Overall, this presents comprehensive technology, addressing its revolutionizing many fields supporting future.

Language: Английский

Citations

35
A UAV-Swarm-Communication Model Using a Machine-Learning Approach for Search-and-Rescue Applications DOI Creative Commons

Hisham Khalil,

Saeed Ur Rahman, Inam Ullah

et al.

Drones, Journal Year: 2022, Volume and Issue: 6(12), P. 372 - 372

Published: Nov. 23, 2022

This paper presents a UAV-swarm-communication model using machine-learning approach for search-and-rescue applications. Firstly, regarding the communication of UAVs, receive signal strength (RSS) and power loss have been modeled random forest regression, mathematical representation channel matrix has also discussed. The second part consisted swarm control modeling UAVs; however, dataset five types triangular formations was generated, K-means clustering applied to predict cluster. In order obtain correct formation, dendrogram all investigated. Finally, heat map contour were plotted kinds clusters. Furthermore, it observed that RSS proposed swarms had good agreement with distances.

Language: Английский

Citations

40
FBC-ANet: A Semantic Segmentation Model for UAV Forest Fire Images Combining Boundary Enhancement and Context Awareness DOI Creative Commons
Lin Zhang, Mingyang Wang, Yunhong Ding

et al.

Drones, Journal Year: 2023, Volume and Issue: 7(7), P. 456 - 456

Published: July 9, 2023

Forest fires are one of the most serious natural disasters that threaten forest resources. The early and accurate identification is crucial for reducing losses. Compared with satellites sensors, unmanned aerial vehicles (UAVs) widely used in fire monitoring tasks due to their flexibility wide coverage. key accurately segment area where located image. However, monitoring, captured remotely by UAVs have characteristics a small area, irregular contour, susceptibility cover, making segmentation areas from images challenge. This article proposes an FBC-ANet network architecture integrates boundary enhancement modules context-aware into lightweight encoder–decoder network. FBC-Anet can extract deep semantic features enhance shallow edge features, thereby achieving effective model uses Xception as backbone encoder different scales images. By transforming extracted through CIA module, model’s feature learning ability pixels enhanced, extraction more robust. decoder BEM module experimental results indicate has better performance target compared baseline model. accuracy on dataset FLAME 92.19%, F1 score 90.76%, IoU reaches 83.08%. indicates indeed valuable related image, segmenting

Language: Английский

Citations

14
Drone Swarm Coordination Using Reinforcement Learning for Efficient Wildfires Fighting DOI
Marc-André Blais, Moulay A. Akhloufi

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(3)

Published: March 13, 2024

Language: Английский

Citations

5
A Novel Swarm Unmanned Aerial Vehicle System: Incorporating Autonomous Flight, Real-Time Object Detection, and Coordinated Intelligence for Enhanced Performance DOI Open Access
Murat Bakırcı

Traitement du signal, Journal Year: 2023, Volume and Issue: 40(5), P. 2063 - 2078

Published: Oct. 30, 2023

Presently, swarm Unmanned Aerial Vehicle (UAV) systems confront an array of obstacles and constraints that detrimentally affect their efficiency mission performance.These include restrictions on communication range, which impede operations across extensive terrains or remote locations; inadequate processing capabilities for intricate tasks such as real-time object detection advanced data analytics; network congestion due to a large number UAVs, resulting in delayed exchange potential failures; power management inefficiencies reducing flight duration overall endurance.Addressing these issues is paramount the successful implementation operation UAV various real-world applications.This paper proposes novel system designed surmount challenges through salient features fortified communication, collaborative hardware integration, task distribution, optimized topology, efficient routing protocols.Cost-effectiveness was prioritized selecting most accessible equipment satisfying minimum requirements, identified comprehensive literature market review.By focusing energy high performance, cooperation facilitated harmonized effective division.The proposed utilizes Raspberry Pi Jetson Nano division, endowing UAVs with superior intelligence navigating environments, detection, execution coordinated actions.The incorporation Ad Hoc Network's decentralized approach enables adaptability expansion response evolving environments demands.An protocol selected system, minimizing unnecessary broadcasting congestion, thereby ensuring extended durations enhanced limited battery capacity.Through careful selection testing software components, improves power, autonomy, scalability, efficiency.This makes it highly adaptable broad spectrum applications.The sets new standard field, demonstrating how integration intelligent hardware, networking can overcome limitations current systems.

Language: Английский

Citations

13
Efficient Detection of Forest Fire Smoke in UAV Aerial Imagery Based on an Improved Yolov5 Model and Transfer Learning DOI Creative Commons
Huanyu Yang, Jun Wang, Jiacun Wang

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(23), P. 5527 - 5527

Published: Nov. 27, 2023

Forest fires pose severe challenges to forest management because of their unpredictability, extensive harm, broad impact, and rescue complexities. Early smoke detection is pivotal for prompt intervention damage mitigation. Combining deep learning techniques with UAV imagery holds potential in advancing fire recognition. However, issues arise when using UAV-derived images, especially detecting miniature patches, complicating effective feature discernment. Common approaches also grapple limitations due sparse datasets. To counter these challenges, we introduce a refined UAV-centric approach utilizing YOLOv5. We first enhance anchor box clustering through K-means++ boost the classification precision then augment YOLOv5 architecture by integrating novel partial convolution (PConv) trim down model parameters elevate processing speed. A unique head incorporated better detect diminutive traces. coordinate attention module embedded within YOLOv5, enabling precise target location fine-grained extraction amidst complex settings. Given scarcity datasets, employ transfer training. The experimental results demonstrate that our proposed method achieves 96% AP50 57.3% AP50:95 on customized dataset, outperforming other state-of-the-art one-stage object detectors while maintaining real-time performance.

Language: Английский

Citations

13
Exploring unmanned aerial systems operations in wildfire management: data types, processing algorithms and navigation DOI Creative Commons
Pirunthan Keerthinathan, A. Narmilan, Grant Hamilton

et al.

International Journal of Remote Sensing, Journal Year: 2023, Volume and Issue: 44(18), P. 5628 - 5685

Published: Sept. 17, 2023

Wildfire, also known as forest fire, is a common natural or man-made disaster that has caused devastation to both structures and ecosystems throughout the world. Unmanned Aerial Systems (UAS)-based remote sensing (RS) provides valuable support for wildfire management efforts, enhancing spatial temporal resolution. The aim of this paper summarize current applications UAS operations combating worldwide. RS been explored during three stages wildfire, including Pre-fire, Active-fire, Post-fire, with particular emphasis on types information collected data processing methods. pre-fire section assesses fire potentials, active-fire stage focuses surveillance propagation, while post-fire studies assessing impact using UAS. In addition, review comprehensive overview navigation techniques adapted surveillance. literature was conducted bibliographic databases, Science Direct, IEEE Explore, Scopus, 186 articles relevant in wildfires were manually gathered. This encompasses 119 focused RS, 40 related navigation, remaining covering reviews, concept proposals, designs, solutions addressing technical limitations UASs context management. offers recent future researchers aiming utilize technologies efficient findings highlight need further investigation into onboard computational capacities Artificial Intelligence (AI) algorithms, precise fuel load estimation, considering individual vegetation, field experiments evaluate validate algorithms.

Language: Английский

Citations

12
The Why and How of Polymorphic Artificial Autonomous Swarms DOI Creative Commons
Fabrice Saffre, Hannu Karvonen, Hanno Hildmann

et al.

Drones, Journal Year: 2025, Volume and Issue: 9(1), P. 53 - 53

Published: Jan. 13, 2025

In this paper, we investigate the concept of polymorphism in context artificial swarms; that is, collectives autonomous platforms such as, for example, unmanned aerial systems. This article provides reader with two practical insights: (a) a proof-of-concept simulation study to show there is clear benefit be gained from considering polymorphic and (b) discussion on design user-friendly human–machine interfaces swarm control enable human operator harness these benefits.

Language: Английский

Citations

0
Dual observers based sliding mode control for QUAVs with unknown disturbances and time varying delays DOI Creative Commons

Chuanfu Liang,

Yuanchun Ding,

Falu Weng

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 14, 2025

Abstract This paper presents a dual-observers-based nonsingular fast terminal sliding mode control scheme for quadrotor unmanned aerial vehicles (QUAVs) with unknown disturbances and time-varying delays. Firstly, to facilitate the controller design, QUAVs model is decoupled into two subsystems: position subsystem attitude subsystem. Secondly, subsystem, presented of QUAVs. For by introducing an exponential term, obtained ensure convergence angles. Moreover, based on singularity problem conventional solved. Thirdly, disturbance delay observers are considering delayed signals disturbances. Finally, effectiveness feasibility proposed demonstrated some computer simulations.

Language: Английский

Citations

0
Enhancing resilience of unmanned autonomous swarms through game theory-based cooperative reconfiguration DOI
Chengxing Wu, Hongzhong Deng, Hongqian Wu

et al.

Reliability Engineering & System Safety, Journal Year: 2025, Volume and Issue: unknown, P. 110951 - 110951

Published: Feb. 1, 2025

Language: Английский

Citations

0