Evaluation of Forest Fire Detection Model using Video captured by UAVs

Dang-Ngoc, Hanh; Nguyen-Trung, Hieu

doi:10.1109/iscit.2019.8905223

Cited by 10 publications

(4 citation statements)

References 17 publications

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“…In [37], spatial, motion, and temporal features are extracted from different regions to identify smoke and fire in IR (Infra-Red) videos. Dang-Ngoc et al [39] presented a two stages method to detect and segment fires in aerial videos. At first, fire color information is extracted using multi-color spaces, RGB, HSI, and YCbCr.…”

Section: Feature-based Fire Detection and Segmentation Methodsmentioning

confidence: 99%

Wildfire Segmentation Using Deep Vision Transformers

Ghali

Akhloufi

Jmal³

et al. 2021

Remote Sensing

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In this paper, we address the problem of forest fires’ early detection and segmentation in order to predict their spread and help with fire fighting. Techniques based on Convolutional Networks are the most used and have proven to be efficient at solving such a problem. However, they remain limited in modeling the long-range relationship between objects in the image, due to the intrinsic locality of convolution operators. In order to overcome this drawback, Transformers, designed for sequence-to-sequence prediction, have emerged as alternative architectures. They have recently been used to determine the global dependencies between input and output sequences using the self-attention mechanism. In this context, we present in this work the very first study, which explores the potential of vision Transformers in the context of forest fire segmentation. Two vision-based Transformers are used, TransUNet and MedT. Thus, we design two frameworks based on the former image Transformers adapted to our complex, non-structured environment, which we evaluate using varying backbones and we optimize for forest fires’ segmentation. Extensive evaluations of both frameworks revealed a performance superior to current methods. The proposed approaches achieved a state-of-the-art performance with an F1-score of 97.7% for TransUNet architecture and 96.0% for MedT architecture. The analysis of the results showed that these models reduce fire pixels mis-classifications thanks to the extraction of both global and local features, which provide finer detection of the fire’s shape.

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Section: Feature-based Fire Detection and Segmentation Methodsmentioning

confidence: 99%

Wildfire Segmentation Using Deep Vision Transformers

Ghali

Akhloufi

Jmal³

et al. 2021

Remote Sensing

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“…Although drones are being used to support environmental emergency response and conservation [38,39,42,[44][45][46]51,52], the majority of the works are empirical studies [41,53,54] or operational models for continuous monitoring of wildland, mission planning, and coordinated task allocation [17,55,56].…”

Section: Related Workmentioning

confidence: 99%

Drone-Based Environmental Emergency Response in the Brazilian Amazon

Custodio,

Abeledo

2023

Drones

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This paper introduces a location–allocation model to support environmental emergency response strategic planning using a drone-based network. Drones are used to verify potential emergencies, gathering additional information to support emergency response missions when time and resources are limited. The resulting discrete facility location–allocation model with mobile servers assumes a centralized network operated out of sight by first responders and government agents. The optimization problem seeks to find the minimal cost configuration that meets operational constraints and performance objectives. To test the practical applicability of the proposed model, a real-life case study was implemented for the municipality of Ji-Paraná, in the Brazilian Amazon, using demand data from a mobile whistle-blower application and from satellite imagery projects that monitor deforestation and fire incidents in the region. Experiments are performed to understand the model’s sensitivity to various demand scenarios and capacity restrictions.

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“…The evaluation of the forest fire detection model using video captured by UAVs is presented in [19]. This paper, to enhance the detection rate, includes forest fire-colored pixels' extraction using chromatic features; and foreground extraction using an optical flow algorithm to examine the motion characteristic of a forest fire.…”

Section: Disaster Managementmentioning

confidence: 99%

Unmanned Aerial Vehicles: A Literature Review

Shaaban¹,

Hassan²,

Gopalakrishnan³

et al. 2022

Journal of Hunan University Natural Sciences

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In recent years, Unmanned Aerial Vehicles (UAVs) have grown and increased in applications because of computational simplicity and adaptive control capacity with strong support from both civilian and military sectors. The applications of UAVs in various military, commercial and civilian areas have led to sustainable results. The application areas include but are not limited to oil & gas, cargo transport, geographic mapping, aerial photography, health care, and disaster management. The success of the UAV application missions is completely dependent on the accuracy in control provided by the flight controllers. Thus, there is a need for accurate, robust, and adaptive flight controllers. UAV dynamics modeling and identification and control of these vehicles are still major active areas of research and development. They pose severe challenges due to the vehicle's complex design, inherently nonlinear, and time-varying dynamics. The main goal of this paper is to identify the past research trends and recent improvements in UAVs. Furthermore, this paper discusses a comprehensive literature review according to the optimized objectives, solution techniques, and applications of UAVs such as Cargo Transport, Disaster Management, etc. According to the literature review, aerial photography is one of the applications of smart UAVs. The reliability of image matching across multiple camera perspectives, angles, and positions encourages computer vision approaches for UAV navigation, opening the way for future researchers to develop vision applications. This article presents a comprehensive literature review discussing the importance of UAV applications related to cost-effectiveness and versatility. Furthermore, a detailed survey of system modeling identification and control techniques is presented.

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Evaluation of Forest Fire Detection Model using Video captured by UAVs

Cited by 10 publications

References 17 publications

Wildfire Segmentation Using Deep Vision Transformers

Wildfire Segmentation Using Deep Vision Transformers

Drone-Based Environmental Emergency Response in the Brazilian Amazon

Unmanned Aerial Vehicles: A Literature Review

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