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An Attention Enhanced Bidirectional LSTM for Early Forest Fire Smoke Recognition
2019
IEEE Access
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In this paper, we propose a novel Attention Enhanced Bidirectional Long Short-Term Memory Network (ABi-LSTM) for video based forest fire smoke recognition. ...
Detecting forest fire smoke during the initial stages is vital for preventing forest fire events. ...
to apply attention mechanism for video-based forest fire smoke recognition. ...
doi:10.1109/access.2019.2946712
fatcat:y234awsvo5c5bomuxhptthll4u
Wildfire Detection via a Dual-Channel CNN with Multi-Level Feature Fusion
2023
Forests
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Second, an attention mechanism, the convolutional block attention module, is used to focus on the key details of the fused features, making the network more efficient. ...
The experimental results show that the accuracy of the proposed model for fire recognition is 98.90%, with a better performance. ...
[50] proposed a dual-stream convolutional neural network based on attention mechanisms, which pays more attention to the spatiotemporal characteristics of smoke and enhances the ability to segment and ...
doi:10.3390/f14071499
fatcat:4srzcxr3t5c3tldj7qebm3ztay
Adversarial Fusion Network for Forest Fire Smoke Detection
2022
Forests
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To cope with these problems, in this paper, we propose an adversarial fusion network (AFN), including a feature fusion network and an adversarial feature-adaptation network for forest fire smoke detection ...
Recent advances suggest that deep learning has been widely used to detect smoke for early forest fire warnings. ...
Therefore, we propose a novel dual-channel convolutional neural network with domain-adversarial training for forest fire smoke detection. ...
doi:10.3390/f13030366
fatcat:dag5kfzbljdoxgq547ewngzk5a
ATT Squeeze U-Net: A Lightweight Network for Forest Fire Detection and Recognition
2021
IEEE Access
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INDEX TERMS Forest fire detection and recognition, attention U-Net, SqueezeNet, fire module, light-weight network. ...
of forest fire. ...
In this article, we propose an efficient neural network architecture for forest fire detection and recognition based on Attention U-Net and SqueezeNet (ATT Squeeze U-Net). ...
doi:10.1109/access.2021.3050628
fatcat:hoyejuq4xfgxdhh4kp5ngqnzne
An Improved Wildfire Smoke Detection Based on YOLOv8 and UAV Images
2023
Sensors
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Forest fires rank among the costliest and deadliest natural disasters globally. Identifying the smoke generated by forest fires is pivotal in facilitating the prompt suppression of developing fires. ...
Thirdly, recognizing the challenge of inadequately capturing salient features of forest fire smoke within intricate wooded settings, this study introduces the BiFormer attention mechanism. ...
[40] proposed a hybrid approach that synergistically combined Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for fire detection in smart urban settings, yielding high accuracy ...
doi:10.3390/s23208374
pmid:37896467
pmcid:PMC10610991
fatcat:zaxpgwzyxzcdhbxitcqzoncwxm
Intelligent Deep Learning Enabled Wild Forest Fire Detection System
2023
Computer systems science and engineering
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Then, the attention based convolution neural network with bidirectional long short term memory (ACNN-BLSTM) model is applied to examine and identify the existence of danger. ...
In this view, this paper presents an intelligent wild forest fire detection and alarming system using deep learning (IWFFDA-DL) model. ...
In addition, the attention based convolution neural network with bidirectional long short term memory (ACNN-BLSTM) is used for the detection of forest fire. ...
doi:10.32604/csse.2023.025190
fatcat:sge6jlbhovhfhkf6zegfzwpzem
Fire-PPYOLOE: An Efficient Forest Fire Detector for Real-Time Wild Forest Fire Monitoring
2024
Journal of Sensors
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Automatic detection and early warning of forest fire in the early stage is very important for protecting forest resources and reducing disaster losses. ...
Unmanned forest fire monitoring is one popular way of forest fire automatic detection. ...
Convolutional neural network (CNN) was first used in smoke and fire image classification [4, [6] [7] [8] . ...
doi:10.1155/2024/2831905
fatcat:j7cc76wmavgh7octtolmdoczqe
Comparison Accuracy of CNN and VGG16 in Forest Fire Identification: A Case Study
2023
Journal of Computer Networks, Architecture and High Performance Computing
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While both models have demonstrated significant promise in visual pattern recognition, a comprehensive analysis regarding their specific benefits in forest fire identification is still needed. ...
While VGG16 exhibits marginally superior performance in identifying forest fires, this discrepancy offers valuable insight into the practical applicability of these two models for fire detection in real-world ...
Convolutional Neural Network To detect forest fires from image data, a Convolutional Neural Network (Cinyol et al., 2023) , (Duarte et al., 2023 ) is a highly effective technology. ...
doi:10.47709/cnahpc.v6i1.3371
fatcat:fsur54pcozghba3gk24otog5um
Forest Fire Smoke Recognition Based on Anchor Box Adaptive Generation Method
2021
Electronics
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Compared with other models, the proposed model can effectively enhance the recognition accuracy and recognition speed of forest fire smoke, which provides a technical basis for the real-time and accurate ...
There are major problems in the field of image-based forest fire smoke detection, including the low recognition rate caused by the changeable and complex state of smoke in the forest environment and the ...
[6] developed a deep normalized convolutional neural network for fire smoke detection, which used a batch of normalization methods to accelerate the training process and enhance the accuracy of smoke ...
doi:10.3390/electronics10050566
fatcat:nugmlydadvaq3m7tgq5nhcxqam
A Small-Target Forest Fire Smoke Detection Model Based on Deformable Transformer for End-to-End Object Detection
2023
Forests
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Traditional forest fire smoke detection based on convolutional neural networks (CNNs) needs many hand-designed components and shows poor ability to detect small and inconspicuous smoke in complex forest ...
Therefore, we propose an improved early forest fire smoke detection model based on deformable transformer for end-to-end object detection (deformable DETR). ...
Convolutional neural networks (CNNs) have become prevalent object detection methods due to their outstanding performance in image recognition [13] . Frizzi et al. ...
doi:10.3390/f14010162
fatcat:twhnukhwcrdotikiptusn7ni2y
Wildfire and Smoke Detection Using Staged YOLO Model and Ensemble CNN
2023
Electronics
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The present research enriches the body of knowledge by evaluating the effectiveness of an efficient wildfire and smoke detection solution implementing ensembles of multiple convolutional neural network ...
One of the most expensive and fatal natural disasters in the world is forest fires. For this reason, early discovery of forest fires helps minimize mortality and harm to ecosystems and forest life. ...
In reference [27] , the authors developed an ABi-LSTM for detecting forest fire smoke. ...
doi:10.3390/electronics12010228
fatcat:7jmumt67gvdjtlhe3edcakxj3e
Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network
2021
Sensors
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The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification ...
To tackle this problem, we propose a multilayered deep convolutional neural network. ...
[18] proposed unmanned aerial vehicle image-based forest fire detection images of forest fires, stabilized the histogram and applied filters to smoothen the images before testing via convolutional neural ...
doi:10.3390/s21082648
pmid:33918922
pmcid:PMC8069408
fatcat:svfjrho4q5bshm5wisnsf3ti2i
Forest Fire Segmentation from Aerial Imagery Data Using an Improved Instance Segmentation Model
2022
Remote Sensing
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The use of drone technology and optimization of existing models to improve forest-fire recognition accuracy and segmentation quality are of great significance for understanding the spatial distribution ...
R-CNN) for incipient forest-fire detection and segmentation based on MS R-CNN model. ...
We also thank the anonymous reviewers for helpful comments and suggestions to this paper.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs14133159
fatcat:6rvwrfdxibhnpn2pmmp2jq237i
A Lightweight CNN Model Based on GhostNet
2022
Computational Intelligence and Neuroscience
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In response to this problem, this paper proposes a lightweight smoke detection model based on the convolutional attention mechanism module. The model is based on the YOLOv5 lightweight framework. ...
The backbone network draws on the GhostNet design idea, replaces the CSP structure of the FPN and head layers with the GhostBottleNeck module, adds a convolutional attention mechanism module to the backbone ...
[47] proposed an energy-saving edge-assisted smoke detection method based on a deep convolutional neural network for foggy monitoring scenes, and the early smoke detection methods outperformed the state-of-the-art ...
doi:10.1155/2022/8396550
pmid:35958795
pmcid:PMC9357762
fatcat:f75wyzg6uzac7nauwn6jfmnh34
PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images
2021
Symmetry
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The target detection of smoke through remote sensing images obtained by means of unmanned aerial vehicles (UAVs) can be effective for monitoring early forest fires. ...
In this paper, we use YOLOX-L as a baseline and propose a forest smoke detection network based on the parallel spatial domain attention mechanism and a small-scale transformer feature pyramid network ( ...
Acknowledgments: We are grateful to all members of the Forestry Information Research Centre for their advice and assistance in the course of this research. ...
doi:10.3390/sym13122260
fatcat:yzzb6horfvbxpfanerfi5ql3ry
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