Forest Monitoring System for Early Fire Detection Based on Convolutional Neural Network and UAV imagery

Georgiev, Georgi; Hristov, Georgi; Zahariev, Plamen; Kinaneva, Diyana

doi:10.1109/telecom50385.2020.9299566

Cited by 32 publications

(16 citation statements)

References 1 publication

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“…Sometimes, the model performance will be reduced if the images failed to match the quality and quantity criteria. The aforementioned object detector was applied by the same scientific team to a UAV platform for Early Forest Fire Detection [29]. Faster R-CNN had also great performance and a threshold of 90% accuracy was applied.…”

Section: Software/methodsmentioning

confidence: 99%

“…In addition, the DroneDeploy [54] and the PIX4D [27] as drone mapping software were used to assist the fire detection procedure. Finally, the open-source Robot Operating System (ROS) [19,49] for drone navigation as well as the Node-Red [29] for programming event-driven applications were also selected.…”

Section: Software/methodsmentioning

confidence: 99%

“…In terms of APIs, in addition to OpenGL, TensorFlow Object Detection was used in four applications [29,30,34,58] and the OpenCV library in two applications [29,50]. Of particular interest is the THEASIS system which is a stand-alone proposed platform for early detection of big wildfires and was implemented in three applications [22,30,34].…”

Section: Software/methodsmentioning

confidence: 99%

“…More specifically, there were ten applications with drones: seven with quadcopters [19][20][21][22][23][24][25], three applications with hexacopter [15,26,27] and one with octacopter [28]. In addition, there were four applications with fixed-wing UAVs [29][30][31][32], while with single-rotor, it was limited to one application [33]. Finally, one application [34] was implemented with 2 UAVs, one fixed wing and one quadcopter.…”

Section: Research Early Statisticsmentioning

confidence: 99%

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Computer Vision for Fire Detection on UAVs—From Software to Hardware

2021

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Fire hazard is a condition that has potentially catastrophic consequences. Artificial intelligence, through Computer Vision, in combination with UAVs has assisted dramatically to identify this risk and avoid it in a timely manner. This work is a literature review on UAVs using Computer Vision in order to detect fire. The research was conducted for the last decade in order to record the types of UAVs, the hardware and software used and the proposed datasets. The scientific research was executed through the Scopus database. The research showed that multi-copters were the most common type of vehicle and that the combination of RGB with a thermal camera was part of most applications. In addition, the trend in the use of Convolutional Neural Networks (CNNs) is increasing. In the last decade, many applications and a wide variety of hardware and methods have been implemented and studied. Many efforts have been made to effectively avoid the risk of fire. The fact that state-of-the-art methodologies continue to be researched, leads to the conclusion that the need for a more effective solution continues to arouse interest.

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Section: Software/methodsmentioning

confidence: 99%

Section: Software/methodsmentioning

confidence: 99%

Section: Software/methodsmentioning

confidence: 99%

Section: Research Early Statisticsmentioning

confidence: 99%

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Computer Vision for Fire Detection on UAVs—From Software to Hardware

2021

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“…The most common use is either by the military [7][8][9] for battlefields or reconnaissance or by civilians for leisure and entertainment [10,11]. Following this, there are many other emerging applications of the UAVs such as agriculture [12,13], environmental protection [14], search and rescue [15], traffic monitoring [16], delivery [17], aerial mapping [18], aerial photography [19] and videography [20,21], fire detection [22].…”

Section: Introductionmentioning

confidence: 99%

A Secure Communication Protocol for Unmanned Aerial Vehicles

Khan¹,

Jhanjhi²,

Brohi³

et al. 2022

Computers, Materials &Amp; Continua

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Mavlink is a lightweight and most widely used open-source communication protocol used for Unmanned Aerial Vehicles. Multiple UAVs and autopilot systems support it, and it provides bi-directional communication between the UAV and Ground Control Station. The communications contain critical information about the UAV status and basic control commands sent from GCS to UAV and UAV to GCS. In order to increase the transfer speed and efficiency, the Mavlink does not encrypt the messages. As a result, the protocol is vulnerable to various security attacks such as Eavesdropping, GPS Spoofing, and DDoS. In this study, we tackle the problem and secure the Mavlink communication protocol. By leveraging the Mavlink packet's vulnerabilities, this research work introduces an experiment in which, first, the Mavlink packets are compromised in terms of security requirements based on our threat model. The results show that the protocol is insecure and the attacks carried out are successful. To overcome Mavlink security, an additional security layer is added to encrypt and secure the protocol. An encryption technique is proposed that makes the communication between the UAV and GCS secure. The results show that the Mavlink packets are encrypted using our technique without affecting the performance and efficiency. The results are validated in terms of transfer speed, performance, and efficiency compared to the literature solutions such as MAVSec and benchmarked with the original Mavlink protocol. Our achieved results have significant improvement over the literature and Mavlink in terms of security.

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