Recent Advances in Fire Detection and Monitoring Systems: A Review

Ghali, Rafik; Jmal, Marwa; Mseddi, Wided Souidene; Attia, Rabah

doi:10.1007/978-3-030-21005-2_32

Cited by 23 publications

(15 citation statements)

References 32 publications

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“…DL approaches are employed for fire detection and segmentation using aerial images. They proved their ability to detect and segment wildfires [6,20]. They can be grouped into three categories: DL approaches for UAV-based fire classification, DL approaches for UAV-based fire detection, and DL approaches for UAV-based fire segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…However, traditional fire detection tools are being replaced by vision-based models that have many advantages such as accuracy, large coverage areas, small probability of errors, and most importantly the ability to work with existing camera surveillance systems. Through the years, researchers have proposed many innovative techniques based on computer vision in order to build accurate fire detection systems [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation

Ghali

Akhloufi

Mseddi

2022

Sensors

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Wildfires are a worldwide natural disaster causing important economic damages and loss of lives. Experts predict that wildfires will increase in the coming years mainly due to climate change. Early detection and prediction of fire spread can help reduce affected areas and improve firefighting. Numerous systems were developed to detect fire. Recently, Unmanned Aerial Vehicles were employed to tackle this problem due to their high flexibility, their low-cost, and their ability to cover wide areas during the day or night. However, they are still limited by challenging problems such as small fire size, background complexity, and image degradation. To deal with the aforementioned limitations, we adapted and optimized Deep Learning methods to detect wildfire at an early stage. A novel deep ensemble learning method, which combines EfficientNet-B5 and DenseNet-201 models, is proposed to identify and classify wildfire using aerial images. In addition, two vision transformers (TransUNet and TransFire) and a deep convolutional model (EfficientSeg) were employed to segment wildfire regions and determine the precise fire regions. The obtained results are promising and show the efficiency of using Deep Learning and vision transformers for wildfire classification and segmentation. The proposed model for wildfire classification obtained an accuracy of 85.12% and outperformed many state-of-the-art works. It proved its ability in classifying wildfire even small fire areas. The best semantic segmentation models achieved an F1-score of 99.9% for TransUNet architecture and 99.82% for TransFire architecture superior to recent published models. More specifically, we demonstrated the ability of these models to extract the finer details of wildfire using aerial images. They can further overcome current model limitations, such as background complexity and small wildfire areas.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation

Ghali

Akhloufi

Mseddi

2022

Sensors

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“…Thanks to its ability to monitor large areas, satellite remote sensing imagery is used to determine and report post-fires, which defines the perimeter of fire areas and the severity of the damage [8]. The damage level is defined by five levels, which are unburned areas with no damages, burned areas with negligible damage, burned areas with moderate damage, burned areas with high damage, and burned areas destroyed.…”

Section: Post-fire Mapping Based Satellite Remote Sensing Imagerymentioning

confidence: 99%

“…DL approaches are used in forest fire segmentation tasks to extract the geometrical characteristics of the fire, such as height, width, angle, and so forth. These models, especially Convolutional Neural Networks (ConvNets), were also successfully employed to predict and detect the boundaries of fire as well as to identify and segment each fire pixel [7,8]. Their impressive results help to develop metrology tools, which can be used in modeling fire preparation as well as providing the necessary inputs to the mathematical propagation models.…”

Section: Introductionmentioning

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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“…Since the rise of deep learning in 2012 (Ghali et al, 2020), it has made outstanding achievements in image classification and object detection, causing a new upsurge in the fields of artificial intelligence and computer vision. Among them, the convolutional neural network is the most outstanding one in image data processing.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Video Flame Detection for Early Fire Warning Based on Deep Learning

et al. 2022

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The widespread use of renewable energy resources requires more immediate and effective fire alarms as a preventive measure. The fire is usually weak in the initial stages, which is not conducive to detection and identification. This paper validates a solution to resolve that problem by a flame detection algorithm that is more sensitive to small flames. Based on Yolov3, the parallel convolution structure of Inception is used to obtain multi-size image information. In addition, the receptive field of the convolution kernel is increased with the dilated convolution so that each convolution output contains a range of information to avoid information omission of tiny flames. The model accuracy has improved by introducing a Feature Pyramid Network in the feature extraction stage that has enhanced the feature fusion capability of the model. At the same time, a flame detection database for early fire has been established, which contains more than 30 fire scenarios and is suitable for flame detection under various challenging scenes. Experiments validate the proposed method not only improves the performance of the original algorithm but are also advantageous in comparison with other state-of-the-art object detection networks, and its false positives rate reaches 1.2% in the test set.

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Recent Advances in Fire Detection and Monitoring Systems: A Review

Cited by 23 publications

References 32 publications

Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation

Deep Learning and Transformer Approaches for UAV-Based Wildfire Detection and Segmentation

Wildfire Segmentation Using Deep Vision Transformers

Multi-Scale Video Flame Detection for Early Fire Warning Based on Deep Learning

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