Intelligent forest fire monitoring system

Štula, Maja; Krstinić, Damir; Šerić, Ljiljana

doi:10.1007/s10796-011-9299-8

Cited by 28 publications

(14 citation statements)

References 10 publications

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“…The systems consist of the imagers, a computer for image processing, the software, and a communication network to transmit the information. Over the years many research in the area of forest fire have been published [3][4][5][6]. Recent research in this area focus on RoTs which are flames and smoke based on the calculation of color indices, In their work the images obtained in the RGB space are transformed into grey scale, a thresholding process is used separating the region of interest from the rest of the image [7].…”

Section: Background Informationmentioning

confidence: 99%

An Autonomous UAS with AI for Forest Fire Prevention, Detection, and Real Time Advice and Communication To and Among Firefighters

EA¹

2017

JCSAIT

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Forest fires are increasing annually due to global warming. The cause of most forest fires are humans, followed by lightning, and winds enabling dry debris, such as small pieces of dry wood that rub against each other creating a spark. Forest fires are catastrophic events that annually claim lives, destroy property, kill domestic and wild animals, and destroy the trees and vegetation that provide oxygen, filter the water, food for animals, and playgrounds and shelter for humans and animals. Additionally after fires burn a forest, rain water runs down the mountain forming mudslides that often times cause damages to land, houses, other building that are on their way, cars, and other property and also kill people, and animals. Therefore forest fires are a disaster having negative effects on people, animals and flora in many ways. In this research paper we provide a solution to preventing forest fires by monitoring the forest using an autonomous UAS powered by solar panels, batteries and a brushless motor, equipped with two high quality cameras in the visual range an infrared camera and a LIDAR, capable of recognizing dry brash, people making camp fires, dead trees, and providing that and other relevant information via wireless transmission to the personnel designated to protect the forest. The intelligent system of the UAS is embedded into a Stratix-10 IntelAltera FPGA that is capable of performing 29.5 teraflops sustained, and is capable of detecting forest fires, as well as providing real time advice to the fire fighters how to efficiently fight the fire. In addition to that the UAS has a router a transceiver and an antenna, enabling the firefighters to communicate with each other and with command and control when the fire destroys the ground antenna system.

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Section: Background Informationmentioning

confidence: 99%

An Autonomous UAS with AI for Forest Fire Prevention, Detection, and Real Time Advice and Communication To and Among Firefighters

EA¹

2017

JCSAIT

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“…This stage is based on the motion detection, image segmentation, and analysis of the dynamic pattern, color-space, and texture. A dedicated processing step is also used to reduce false alarms [23].…”

Section: A Machine Vision Techniques For Smoke Detectionmentioning

confidence: 99%

“…A matrix D P (x, y, t) that contains the values of R P (i, t) related to each pixel is then computed. An approach for false alarms reduction based on a similar principle is described in [23].…”

Section: Ieee Transactions On Systems Man and Cybernetics-part A: Symentioning

confidence: 99%

Wildfire Smoke Detection Using Computational Intelligence Techniques Enhanced With Synthetic Smoke Plume Generation

Labati

Genovese

Piuri

et al. 2013

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-An early wildfire detection is essential in order to assess an effective response to emergencies and damages. In this paper, we propose a low cost approach based on image processing and computational intelligence techniques, capable to adapt and identify wildfire smoke from heterogeneous sequences taken from a long distance. Since the collection of frame sequences can be difficult and expensive, we propose a virtual environment, based on a cellular model, for the computation of synthetic wildfire smoke sequences. The proposed detection method is tested on both real and simulated frame sequences. Results show that the proposed approach obtains accurate results.

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“…The main iForestFire sensor for wildfire detection is a video camera sensible in the visible spectra during the day and near IR spectra during the night, but a certain fusion of video data with meteorological data and meteorological simulations are also used [7,14,16], however we will not discuss them here. In the video analysis part, the early detection of wildfires is primarily based on the detection of the wildfire smoke in images during the day, because in early wildfire stage the fire flames are usually not visible in daylight, and the detection of wildfire flames in images during the night.…”

Section: Fig 3 Examples Of Field Units Deployed In National Parksmentioning

confidence: 99%

“…Since the beginning it was conceived as a cloud computing based application, having theoretical background in the formal theory of perception and agent based software architecture [3,5,6,7,8,9,20]. Every year certain improvements and enhancements have been introduced on both automatic wildfire detection [10,11,13,14,16,18,19], and manual mode used for distant video presence [12,15,17]. This paper describes our last improvements in automatic wildfire video detection part.…”

Section: Introductionmentioning

confidence: 99%

Vision based wildfire and natural risk observers

Stipaničev

Šerić

Braović

et al. 2012

2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)

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Wildfires are natural risk phenomena that cause significant economic and environmental damage. In wildfire fighting strategy it is important to detect the wildfire in its initial stage and to apply, as soon as possible, the most appropriate fire fighting action. In both cases wildfire monitoring and surveillance systems are of great importance, so in the last decade the interest for various wildfire monitoring and surveillance systems has increased, both on the research and the implementation level. This paper describes one such system named iForestFire. It is an example of advanced terrestrial vision based wildfire monitoring and surveillance system, today widely used in various Croatian National and Nature Parks and regions, but it is also a system in constant development and improvement, both on theoretical and practical level. This paper describes its last improvements in video detection part that are based on notation of observer, cogent confabulation theory and mechanism of thought. Inclusion of cogent confabulation theory allows us to expend the use of existing wildfire observers to more general natural risk observers.

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Intelligent forest fire monitoring system

Cited by 28 publications

References 10 publications

An Autonomous UAS with AI for Forest Fire Prevention, Detection, and Real Time Advice and Communication To and Among Firefighters

An Autonomous UAS with AI for Forest Fire Prevention, Detection, and Real Time Advice and Communication To and Among Firefighters

Wildfire Smoke Detection Using Computational Intelligence Techniques Enhanced With Synthetic Smoke Plume Generation

Vision based wildfire and natural risk observers

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