4.4 Article

Forest fire and smoke detection using deep learning-based learning without forgetting

Related references

Note: Only part of the references are listed.
Article Automation & Control Systems

Time-Efficient Fire Detection Convolutional Neural Network Coupled with Transfer Learning

Hanan A. Hosni Mahmoud et al.

Summary: This research presents a time-efficient fire detection convolutional neural network for surveillance systems. The model utilizes CNN architecture and proves its accuracy and time efficiency in fire detection through extensive experiments. It is compared to state-of-the-art fire detection models.

INTELLIGENT AUTOMATION AND SOFT COMPUTING (2022)

Review Engineering, Electrical & Electronic

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

Abdelmalek Bouguettaya et al.

Summary: The study focused on early detection of wildfires in forest and wildland areas using deep learning-based computer vision algorithms to prevent and reduce disastrous losses in terms of human lives and forest resources.

SIGNAL PROCESSING (2022)

Article Multidisciplinary Sciences

Forest fire detection system using wireless sensor networks and machine learning

Udaya Dampage et al.

Summary: Forest fires have become a major threat to both the environment and ecosystem, and it is crucial to detect fires at their initial stage. This paper proposes a system and methodology that utilizes wireless sensor networks and machine learning regression models to detect forest fires in their early stages. The system has shown effective fire detection capabilities in real-life trials.

SCIENTIFIC REPORTS (2022)

Article Engineering, Electrical & Electronic

Fire-Net: A Deep Learning Framework for Active Forest Fire Detection

Seyd Teymoor Seydi et al.

Summary: Forest conservation is vital for maintaining a healthy ecosystem. Remote sensing technology, combined with computer vision and sensor technologies, plays a crucial role in monitoring forest land, especially in the detection of active forest fires. This paper presents a deep learning framework, Fire-Net, which uses Landsat-8 imagery to accurately detect active fires and burning biomass.

JOURNAL OF SENSORS (2022)

Article Computer Science, Information Systems

An Effective Forest Fire Detection Framework Using Heterogeneous Wireless Multimedia Sensor Networks

Burak Kizilkaya et al.

Summary: With improvements in IoT, surveillance systems have become more accessible. This study proposes a hierarchical approach for forest fire detection, minimizing visual data transmission by using multimedia and scalar sensors hierarchically. A lightweight deep learning model is developed to improve detection accuracy and reduce traffic.

ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS (2022)

Article Computer Science, Artificial Intelligence

Attention based CNN model for fire detection and localization in real-world images

Saima Majid et al.

Summary: This paper presents an automated fire detection system using computer vision, which utilizes transfer learning with state-of-the-art CNNs and the Grad-CAM method for visualization and localization of fire. The EfficientNetB0 network emerged as the best-suited choice for detecting fire in real-world images, achieving high efficiency and reliability with a test accuracy of 95.40% and a recall of 97.61%.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

Article Computer Science, Artificial Intelligence

Fast forest fire smoke detection using MVMNet

Yaowen Hu et al.

Summary: In this paper, a forest fire detection method based on multioriented detection and value conversion-attention mechanism module is proposed. The method calculates the rotation angle of the target using an angle parameter and classification prediction method, addressing the issue of determining the direction of the fire source. Softpool-spatial pyramid pooling and value conversion-attention mechanism module are used to extract smoke color and texture information. Finally, a hybrid nonmaximum suppression method is employed to address the issues of smoke false detection and missed detection.

KNOWLEDGE-BASED SYSTEMS (2022)

Article Green & Sustainable Science & Technology

Fire-YOLO: A Small Target Object Detection Method for Fire Inspection

Lei Zhao et al.

Summary: The improved Fire-YOLO deep learning algorithm effectively detects small targets, fire-like and smoke-like targets in forest fire images, and achieves real-time fire detection under different natural lights, enhancing network performance.

SUSTAINABILITY (2022)

Article Computer Science, Hardware & Architecture

A Lightweight Hierarchical AI Model for UAV-Enabled Edge Computing with Forest-Fire Detection Use-Case

Mostafa M. Fouda et al.

Summary: In this article, a lightweight hierarchical artificial intelligence framework is proposed to address the limited computational resources in unmanned aerial vehicle data acquisition. By formulating a multi-objective optimization problem and using TOPSIS technique, the framework achieves a Pareto-optimal solution for early forest-fire detection with high accuracy and reduced computational burden.

IEEE NETWORK (2022)

Review Computer Science, Artificial Intelligence

An automatic fire detection system based on deep convolutional neural networks for low-power, resource-constrained devices

Pedro Vinicius A. B. de Venancio et al.

Summary: Large-scale fires are increasingly reported, driving the search for effective solutions. A promising approach is a computer vision based automatic system that detects fire early and enables rapid suppression. Current effective systems use convolutional neural networks (CNNs), but they are computationally expensive. We propose a low-power CNN-based fire detector system that reduces computational cost without compromising performance.

NEURAL COMPUTING & APPLICATIONS (2022)

Article Computer Science, Artificial Intelligence

Automated accurate fire detection system using ensemble pretrained residual network

Sengul Dogan et al.

Summary: This work develops an accurate fire warning model using images, utilizing two new deep feature engineering models and pretrained ResNet networks for feature extraction. Support vector machine classifiers and ensemble models are used for classification. The developed models achieve high classification accuracy and further testing on larger databases is recommended.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

Article Engineering, Multidisciplinary

A Systematic Review of Applications of Machine Learning Techniques for Wildfire Management Decision Support

Karol Bot et al.

Summary: This paper provides a review of recent applications of machine learning methods for wildfire management decision support, summarizing these applications based on case study type, machine learning method, case study location, and performance metrics. It is concluded that the adoption of machine learning methods can enhance support in different fire management phases.

INVENTIONS (2022)

Article Engineering, Multidisciplinary

Multi-Scale Prediction For Fire Detection Using Convolutional Neural Network

Myeongho Jeon et al.

Summary: Automation of fire detection systems can reduce loss of life and property, with recent studies showing that convolutional neural networks outperform conventional image processing methods. However, previous studies had limitations in classifying fire images of various sizes, which led to the proposal of a multi-scale prediction framework utilizing feature maps from all scales and a feature-squeeze block for effective information utilization. Extensive evaluations showed that this method outperformed state-of-the-art convolutional neural networks, with a 97.89% F1-score and 0.0227 false positive rate in multiple evaluations.

FIRE TECHNOLOGY (2021)

Article Ecology

Predicting wildfire impacts on the prehistoric archaeological record of the Jemez Mountains, New Mexico, USA

Megan M. Friggens et al.

Summary: Wildfires of uncharacteristic severity, caused by climate changes and accumulated fuels, can have amplified or novel impacts on archaeological resources. Machine learning models identified topography and pre-fire weather and fuel condition as important predictors of fire effects and severity at archaeological sites. Models for predicting negative impacts of fires on the archaeological record can help prioritize fuel treatments and guide post-fire rehabilitation efforts for cultural resource preservation.

FIRE ECOLOGY (2021)

Article Ecology

A Deep Learning Based Object Identification System for Forest Fire Detection

Federico Guede-Fernandez et al.

Summary: This paper presents the design and validation of a system for the classification of smoke columns, with different smoke classes used during dataset labelling and achieving good performance on an independent test set. The models performed satisfactorily in detecting smoke columns in real fire scenarios.

FIRE-SWITZERLAND (2021)

Article Automation & Control Systems

Fire Detection Method Based on Depthwise Separable Convolution and YOLOv3

Yue-Yan Qin et al.

Summary: This study proposes a method for fire detection that combines the classification model and target detection model in deep learning, using depthwise separable convolution and YOLOv3 model to effectively improve detection accuracy and speed.

INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING (2021)

Review Engineering, Multidisciplinary

A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems

Faroudja Abid

Summary: Forest fires are a major environmental concern globally, causing economic, ecological damage, and loss of human lives. The trend now is towards integrating artificial intelligence into fire prediction and detection systems. Research focuses on machine learning algorithms and factors influencing fire occurrence and risk.

FIRE TECHNOLOGY (2021)

Article Computer Science, Information Systems

Convolutional neural network based early fire detection

Faisal Saeed et al.

MULTIMEDIA TOOLS AND APPLICATIONS (2020)

Article Computer Science, Artificial Intelligence

Wildfire detection using transfer learning on augmented datasets

Maria Joao Sousa et al.

EXPERT SYSTEMS WITH APPLICATIONS (2020)

Article Thermodynamics

Image fire detection algorithms based on convolutional neural networks

Pu Li et al.

CASE STUDIES IN THERMAL ENGINEERING (2020)

Article Computer Science, Theory & Methods

Exploring the efficacy of transfer learning in mining image-based software artifacts

Natalie Best et al.

JOURNAL OF BIG DATA (2020)

Article Computer Science, Artificial Intelligence

An Efficient Fire Detection Method Based on Multiscale Feature Extraction, Implicit Deep Supervision and Channel Attention Mechanism

Songbin Li et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

Article Computer Science, Information Systems

Non-Temporal Lightweight Fire Detection Network for intelligent Surveillance Systems

Hunjun Yang et al.

IEEE ACCESS (2019)

Article Computer Science, Information Systems

Fire smoke detection algorithm based on motion characteristic and convolutional neural networks

Yanmin Luo et al.

MULTIMEDIA TOOLS AND APPLICATIONS (2018)

Article Engineering, Multidisciplinary

Fire Recognition Based On Multi-Channel Convolutional Neural Network

Wentao Mao et al.

FIRE TECHNOLOGY (2018)

Article Computer Science, Artificial Intelligence

Early fire detection using convolutional neural networks during surveillance for effective disaster management

Khan Muhammad et al.

NEUROCOMPUTING (2018)

Article Computer Science, Information Systems

Convolutional Neural Networks Based Fire Detection in Surveillance Videos

Khan Muhammad et al.

IEEE ACCESS (2018)

Article Telecommunications

IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety

Faisal Saeed et al.

JOURNAL OF SENSOR AND ACTUATOR NETWORKS (2018)

Article Computer Science, Artificial Intelligence

An Efficient Deep Learning Algorithm for Fire and Smoke Detection with Limited Data

Abdulaziz Namozov et al.

ADVANCES IN ELECTRICAL AND COMPUTER ENGINEERING (2018)

Article Computer Science, Hardware & Architecture

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky et al.

COMMUNICATIONS OF THE ACM (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Xception: Deep Learning with Depthwise Separable Convolutions

Francois Chollet

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

Review Computer Science, Artificial Intelligence

Deep learning in neural networks: An overview

Juergen Schmidhuber

NEURAL NETWORKS (2015)

Article Engineering, Civil

Fire detection based on vision sensor and support vector machines

Byoung Chul Ko et al.

FIRE SAFETY JOURNAL (2009)