Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires

Sudhakar, S.; Vijayakumar, V.; Kumar, C. Sathiya; Priya, V.; Ravi, Logesh; Subramaniyaswamy, V.

doi:10.1016/j.comcom.2019.10.007

Cited by 206 publications

(84 citation statements)

References 19 publications

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“…Sudhakar et al [ 79 ] proposed a method for forest fire detection through UAVs equipped with an optical and an infrared camera. They used a LAB color model and a motion-based algorithm followed by a maximally stable extremal regions (MSERs) extraction module.…”

Section: Early Fire Detection Systemsmentioning

confidence: 99%

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Barmpoutis

Papaioannou

Dimitropoulos

et al. 2020

Sensors

285

106

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The environmental challenges the world faces nowadays have never been greater or more complex. Global areas covered by forests and urban woodlands are threatened by natural disasters that have increased dramatically during the last decades, in terms of both frequency and magnitude. Large-scale forest fires are one of the most harmful natural hazards affecting climate change and life around the world. Thus, to minimize their impacts on people and nature, the adoption of well-planned and closely coordinated effective prevention, early warning, and response approaches are necessary. This paper presents an overview of the optical remote sensing technologies used in early fire warning systems and provides an extensive survey on both flame and smoke detection algorithms employed by each technology. Three types of systems are identified, namely terrestrial, airborne, and spaceborne-based systems, while various models aiming to detect fire occurrences with high accuracy in challenging environments are studied. Finally, the strengths and weaknesses of fire detection systems based on optical remote sensing are discussed aiming to contribute to future research projects for the development of early warning fire systems.

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Section: Early Fire Detection Systemsmentioning

confidence: 99%

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Barmpoutis

Papaioannou

Dimitropoulos

et al. 2020

Sensors

285

106

View full text Add to dashboard Cite

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“…Fire detection is often achieved by analyzing imagery captured by satellites and aerial platforms equipped with multi-band sensors [27]. Researchers initially analyzed the data by detecting the spectral signature, or color property, of fire [28].…”

Section: Fire Detection Via Machine Learning and Deep Learning Methodsmentioning

confidence: 99%

Deep Learning Based Wildfire Event Object Detection from 4K Aerial Images Acquired by UAS

Tang

Liu

Chen

et al. 2020

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Unmanned Aerial Systems, hereafter referred to as UAS, are of great use in hazard events such as wildfire due to their ability to provide high-resolution video imagery over areas deemed too dangerous for manned aircraft and ground crews. This aerial perspective allows for identification of ground-based hazards such as spot fires and fire lines, and to communicate this information with fire fighting crews. Current technology relies on visual interpretation of UAS imagery, with little to no computer-assisted automatic detection. With the help of big labeled data and the significant increase of computing power, deep learning has seen great successes on object detection with fixed patterns, such as people and vehicles. However, little has been done for objects, such as spot fires, with amorphous and irregular shapes. Additional challenges arise when data are collected via UAS as high-resolution aerial images or videos; an ample solution must provide reasonable accuracy with low delays. In this paper, we examined 4K ( 3840 × 2160 ) videos collected by UAS from a controlled burn and created a set of labeled video sets to be shared for public use. We introduce a coarse-to-fine framework to auto-detect wildfires that are sparse, small, and irregularly-shaped. The coarse detector adaptively selects the sub-regions that are likely to contain the objects of interest while the fine detector passes only the details of the sub-regions, rather than the entire 4K region, for further scrutiny. The proposed two-phase learning therefore greatly reduced time overhead and is capable of maintaining high accuracy. Compared against the real-time one-stage object backbone of YoloV3, the proposed methods improved the mean average precision(mAP) from 0 . 29 to 0 . 67 , with an average inference speed of 7.44 frames per second. Limitations and future work are discussed with regard to the design and the experiment results.

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“…The lack of information about the current state and the dynamic evolution of fire plays a central role in the accidents. Nowadays the demand increases for remote monitoring of this natural disaster [ 2 , 18 , 19 , 20 ]. For that, artificial visual control is a new area that has gained interest.…”

Section: Motivationmentioning

confidence: 99%

A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

Tlig¹,

Bouchouicha

Tlig

et al. 2020

Sensors

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Forests provide various important things to human life. Fire is one of the main disasters in the world. Nowadays, the forest fire incidences endanger the ecosystem and destroy the native flora and fauna. This affects individual life, community and wildlife. Thus, it is essential to monitor and protect the forests and their assets. Nowadays, image processing outputs a lot of required information and measures for the implementation of advanced forest fire-fighting strategies. This work addresses a new color image segmentation method based on principal component analysis (PCA) and Gabor filter responses. Our method introduces a new superpixels extraction strategy that takes full account of two objectives: regional consistency and robustness to added noises. The novel approach is tested on various color images. Extensive experiments show that our method obviously outperforms existing segmentation variants on real and synthetic images of fire forest scenes, and also achieves outstanding performance on other popular benchmarked images (e.g., BSDS, MRSC). The merits of our proposed approach are that it is not sensitive to added noises and that the segmentation performance is higher with images of nonhomogeneous regions.

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Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires

Cited by 206 publications

References 19 publications

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Deep Learning Based Wildfire Event Object Detection from 4K Aerial Images Acquired by UAS

A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

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