A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms

Bouguettaya, Abdelmalek; Zarzour, Hafed; Taberkit, Amine Mohammed; Kechida, Ahmed

doi:10.1016/j.sigpro.2021.108309

Cited by 112 publications

(43 citation statements)

References 46 publications

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“…Traditional computer vision approaches such as median/Gaussian filtering, image segmentation, and color analysis (in both RGB or HSV spaces) have been successfully applied to perform smoke detection and successive identification of the fire location in terms of altitude, latitude, and longitude [164,165]. When the ground control stations are equipped with significant computational power, deep learning algorithms such as convolutional neural networks and deep neural networks with an underling YOLOv3 architecture can be used to achieve improved flame and smoke detection performance at reduced false-alarm rates, even in the presence of adverse cloud and sunlight conditions, as well as undesired reflections from objects in the scene [166][167][168]. Image/video-based analytics have proven their effectiveness mainly for wildfires localized in relatively small areas.…”

Section: Uav For Land Monitoringmentioning

confidence: 99%

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Fascista

2022

Sensors

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Fighting Earth’s degradation and safeguarding the environment are subjects of topical interest and sources of hot debate in today’s society. According to the United Nations, there is a compelling need to take immediate actions worldwide and to implement large-scale monitoring policies aimed at counteracting the unprecedented levels of air, land, and water pollution. This requires going beyond the legacy technologies currently employed by government authorities and adopting more advanced systems that guarantee a continuous and pervasive monitoring of the environment in all its different aspects. In this paper, we take the research on integrated and large-scale environmental monitoring a step further by providing a comprehensive review that covers transversally all the main applications of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and crowdsensing monitoring technologies. By outlining the available solutions and current limitations, we identify in the cooperation among terrestrial (WSN/crowdsensing) and aerial (UAVs) sensing, coupled with the adoption of advanced signal processing techniques, the major pillars at the basis of future integrated (air, land, and water) and large-scale environmental monitoring systems. This review not only consolidates the progresses achieved in the field of environmental monitoring, but also sheds new lights on potential future research directions and synergies among different research areas.

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Section: Uav For Land Monitoringmentioning

confidence: 99%

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Fascista

2022

Sensors

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“…These flying machines can be controlled remotely using remote controller devices, or perform some missions autonomously through an onboard computer that executes intelligent algorithms. Recently, smart UAVs are widely applied to perform a wide range of applications, including search and rescue operations Martinez-Alpiste et al [ 52 ], wildfire detection Bouguettaya et al [ 20 ], vehicle detection Bouguettaya et al [ 19 ], precision agriculture Di Nisio et al [ 27 ], Delavarpour et al [ 26 ], package delivery Shahzaad et al [ 64 ], smart cities Abualigah et al [ 5 ], to name a few.…”

Section: An Overview On Agricultural Unmanned Aerial Vehicles and The...mentioning

confidence: 99%

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

et al. 2022

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The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers’ naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.

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“…The process of images collection was performed through searching over the Internet about Wildfire images taking into consideration the following concepts; view angles (Top, Side), perspectives (Day, Night) addition to variant size of images and distances for more significant variance. We have faced the problem of limited Wildfire images required for training mode [13,17], and there is no standard wildfire dataset to utilize [4,18].…”

Section: Images Collectionmentioning

confidence: 99%

An Edge Computing Environment for Early Wildfire Detection

Mahdi¹,

Mahmood²

2022

AETiC

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Recently, an increasing demand is growing for installing a rapid response system in forest regions to enable an immediate and appropriate response to wildfires before they spread across vast areas. This paper introduces a multilevel system for early wildfire detection to support public authorities to immediately specify and attend to emergency demands. The presented work is designed and implemented within Edge Computing Infrastructure. At the first level; the dataset samples of wildfire represented by a set of video sequences are collected and labelled for training mode purposes. Then, YOLOv5 deep learning model is adopted in our framework to build a trained model for distinguishing the fire event against non-fire events in binary classification. The proposed system structure comprises IoT entities provided with camera sensor capabilities and NVIDIA Jetson Nano Developer kit as an edge computing environment. At the first level, a video camera is employed to assemble environment information received by the micro-controller middle level to handle and detect the possible fire event presenting in the interested area. The last level is characterized as making a decision by sending a text message and snapshot images to the cloud server. Meanwhile, a set of commands are sent to IoT nodes to operate the speakers and sprinklers, which are strategically assumed to place on the ground to give an alarm and prevent wildlife loss. The proposed system was tested and evaluated using a wildfire dataset constructed by our efforts. The experimental results exhibited 98% accurate detection of fire events in the video sequence. Further, a comparison study is performed in this research to confirm the results obtained from recent methods.

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A review on early wildfire detection from unmanned aerial vehicles using deep learning-based computer vision algorithms

Cited by 112 publications

References 46 publications

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

An Edge Computing Environment for Early Wildfire Detection

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