Early Forest Fire Detection Research Articles

Comparative Analysis of Deep Learning Algorithms in Fire Detection

As technology advances rapidly, deep learning applications, a subset of machine learning, are becoming increasingly relevant in various aspects of our lives. Essential daily applications like license plate recognition and optical character recognition are now commonplace. Alongside current technological progress, the development of future-integrated technologies such as suspicious situation detection from security cameras and autonomous vehicles is also accelerating. The success and accuracy of these technologies have reached impressive levels. This study focuses on the early and accurate detection of forest fires before they cause severe damage. Using primarily forest fire images from datasets obtained from Kaggle, various deep learning algorithms were trained via transfer learning using MATLAB. This approach allowed for comparing different deep learning algorithms based on their efficiency and accuracy in detecting forest fires. High success rates, generally exceeding 90%, were achieved.

Deep Learning Approach for Wildland Fire Recognition Using RGB and Thermal Infrared Aerial Image

Wildfires cause severe consequences, including property loss, threats to human life, damage to natural resources, biodiversity, and economic impacts. Consequently, numerous wildland fire detection systems were developed over the years to identify fires at an early stage and prevent their damage to both the environment and human lives. Recently, deep learning methods were employed for recognizing wildfires, showing interesting results. However, numerous challenges are still present, including background complexity and small wildfire and smoke areas. To address these challenging limitations, two deep learning models, namely CT-Fire and DC-Fire, were adopted to recognize wildfires using both visible and infrared aerial images. Infrared images detect temperature gradients, showing areas of high heat and indicating active flames. RGB images provide the visual context to identify smoke and forest fires. Using both visible and infrared images provides a diversified data for learning deep learning models. The diverse characteristics of wildfires and smoke enable these models to learn a complete visual representation of wildland fires and smoke scenarios. Testing results showed that CT-Fire and DC-Fire achieved higher performance compared to baseline wildfire recognition methods using a large dataset, which includes RGB and infrared aerial images. CT-Fire and DC-Fire also showed the reliability of deep learning models in identifying and recognizing patterns and features related to wildland smoke and fires and surpassing challenges, including background complexity, which can include vegetation, weather conditions, and diverse terrain, detecting small wildfire areas, and wildland fires and smoke variety in terms of size, intensity, and shape. CT-Fire and DC-Fire also reached faster processing speeds, enabling their use for early detection of smoke and forest fires in both night and day conditions.

Digital Measures to Decipher Forest Fires a Comparative Study of the Moroccan and American Experiences

Objectives: The study analyzed the digital measures in forest fire management in Morocco and the United States, comparing of the fire management methods in each country. The goal was to enhance the policies and strategies used in Morocco and to further integrate the Moroccan approach with the American experience. Methods: The study relied on the descriptive-analytical approach to describe the digital measures adopted in both Morocco and the United States for extinguishing forest fires. The comparative approach was also used to compare the experiences of both countries in combating forest fires and explore how Morocco can benefit from American expertise. Results: The study results indicate that both Morocco and the United States use digital technology for early detection of forest fires. However, Morocco faces challenges such as funding shortages, inadequate technical infrastructure, and the need for firefighter training. Meanwhile, the U.S. employs advanced technologies, including satellites, drones, and ground-based thermal sensors, and benefits from integrated information management systems and artificial intelligence. Despite this, the U.S. also encounters challenges related to coordinating efforts among different agencies. In comparison, the U.S. has significantly higher levels of funding and technical support, allowing for the use of more advanced technologies. Therefore, Morocco could benefit from the American experience in several ways, including improving technical infrastructure, training firefighting teams, using integrated information management systems, and exchanging expertise and knowledge.. Conclusions: These results reflect the importance of digital technology in enhancing the effectiveness of forest fire response and highlight Morocco's potential to benefit from the United States' experience to address this environmental issue more effectively.

Towards early forest fire detection and prevention using AI-powered drones and the IoT

Wildfires are a significant natural hazard, resulting in financial losses, human deaths, and environmental damage. Due to the rising severity and frequency of wildfires, wildfire management and detection have recently received increased attention worldwide. Monitoring potential risk areas and early fire detection are critical factors for shortening the reaction time and reducing the potential damage. Conventional wildfire detection techniques like satellite imaging and remote camera-based sensing need more latency and low reliability. To tackle these limitations, this paper proposes a novel airborne UAV-based IoT (UIoT) system for wildfire sensing, detection, and extinguishing. It presents the design of low-cost and low-maintenance fire-detecting IoT nodes for large-scale deployment. It also proposes, investigates, and reports on several connectivity architectures using the LoRaWAN protocol for UIoT systems. We geolocate forest trees in a terrain-mapping exercise for precise fire localization. We then deploy an autonomous drone with a visual camera that utilizes our novel classification network to detect the presence of fire followed by fire detection and particle filter-based tracking to center fire at the center of the frame. We use a cloud back-end server for monitoring, analysis, and reporting. Our results show that our low-cost and long-range IoT nodes accurately detect fire within 1-5 min after fire ignition. Our fire classification network achieved an accuracy of 99.46% and a mean average precision of 99.64%. Numerical results suggest that the proposed UAV-IoT-based fire classification offers a fast and reliable wildfire recognition and extinguishing solution while minimizing power consumption and increasing the battery lifespan.

Using Cognitive IOT In Vehicle System for On Actual Time Monitoring of CO2 Emission with Temperature and Alcohol Detector

One of the pressing environmental challenges we face today is the phenomenon of global warming, primarily driven by the release of greenhouse gases. Among these gases, carbon dioxide stands out as a significant contributor, with its emissions leading to a warming effect on the Earth's surface. Protecting our environment requires us to address and manage these changes effectively, presenting a considerable challenge. For long-term control of carbon dioxide emissions at their source, there is a growing recognition of the need for preventive and control technologies. This paper aims to employ IoT technology, utilizing Raspberry Pi's CO2 sensitivity, to monitor emissions from various sources such as public transportation, industries, and forest fires. Continuous monitoring of carbon dioxide levels within a city will help identify areas with the highest pollution levels. Additionally, this initiative seeks to implement an intelligent system for early detection of forest fires, also known as wildfires. These uncontrolled fires in natural areas pose significant threats to both ecosystems and human resources. It's noteworthy that wildfires emit more CO2 gas than initially assumed, impacting state climate targets. Integrating these efforts with IoT enhances security and enables various services, including the deployment of a Simple Notification Service (SNS) to alert mobile users about high CO2 levels in specific areas. Furthermore, this model aims to extend its capabilities to include the detection of temperature in heated vehicle components, such as engines, using temperature detectors. Moreover, alcohol detectors will be utilized to monitor alcohol consumption by vehicle drivers, enhancing safety measures on the road. Keywords: SNS (Simple Notification Service), IoT (Internet of Things), GPS (Global Positioning System), GVG (Green Vehicle Guide), MAQUMON (Mobile Air Quality Monitoring Network).

Analysis of the Effectiveness of Using Two-Stage Neural Network Models for Early Detection of Forest Fires

The purpose of the research – analysis of the effectiveness of two-stage neural network models for solving the problem of detecting forest fires in images obtained from unmanned aerial vehicles.Methods. А training dataset was synthesized for training neural network models for the purpose of detection and semantic segmentation of forest fires in images. Тwo-stage neural network models (“Faster R-CNN”, “Mask RCNN” and “Retina-Net”) were used for training. Тhe neural network models were trained according to the same parameters set for all models in order to ensure consistency and a common basis for experiments. Optimization of model parameters during the training process was carried out to minimize the classification loss function. Тo synthesize the test sample, we used a video sequence covering the events of forest fires in the /rkutsk region, which was filmed by an unmanned aerial vehicle. Using a specially developed script in the Рython programming language, the process of dividing this video sequence into separate frames was carried out, which were used as a test data set when assessing the quality of classification of trained neural network models.Results. Based on the analysis of the obtained values of the quality criterion, as well as visual analysis on the test data set produced as part of testing neural network models, the effectiveness of the studied models for detecting forest fires in images was assessed. Тo assess the quality of binary classification of neural network models, the quality criterion “Accuracy” (classification accuracy) was used.Conclusion. Еxperimental studies on a test data set showed that the Retina-Net model demonstrates the lowest, but acceptable, performance compared to other studied neural network models. Тhe two-stage neural network models “Faster R-CNN” and “Mask R-CNN” demonstrate similar classification accuracy values (0.9492 and 0.9521, respectively), which allows us to recommend them for use in early detection systems for forest fires.

ISSUES OF CREATING A SYSTEM FOR EARLY DETECTION OF FOREST FIRES BASED ON UNMANNED AERIAL VEHICLES

The urgent importance of the task of monitoring the state of forests lies in the danger of forest fires. Forest fires are a frequent occurrence in almost all continents. Despite the high information content of the spectral signs of a forest fire, the corresponding functionality is not fully used in the known implementations of the forest fire early detection system. The spectral region in which it is possible to distinguish forest areas in a state of fire from their usual state is quite wide and covers the middle and thermal zone of the infrared part of the spectrum. The installation of spectral equipment (multi- and hyperspectrometers) on board the UAV (unmanned aerial vehicle) can certainly expand the functionality of unmanned equipment in the early detection of forest fires. The optimal procedure for using UAVs equipped with hyperspectral equipment for early detection of forest fires is proposed. An optimization problem has been formulated and solved to determine the most appropriate mode of changing the UAV flight altitude with an increase in the wavelength of the measurements being carried out. According to the proposed method, on the right branch of the Planck curve, with increasing wavelength, the flight altitude should be increased, and on the left branch it should be reduced. This order corresponds to the case of increasing the wavelength on both branches and follows from the integral restriction set on the function of the dependence of the flight altitude on the wavelength, which comes from the limited real energy resources on board the UAV. Based on the developed method, a new technique for early detection of forest fires based on the use of hyperspectral method is proposed.

IoT Based Early Forest Fire Detection and Monitoring System

IoT Based Early Forest Fire Detection and Monitoring System

Development of a deep learning-based surveillance system for forest fire detection and monitoring using UAV.

This study presents a surveillance system developed for early detection of forest fires. Deep learning is utilized for aerial detection of fires using images obtained from a camera mounted on a designed four-rotor Unmanned Aerial Vehicle (UAV). The object detection performance of YOLOv8 and YOLOv5 was examined for identifying forest fires, and a CNN-RCNN network was constructed to classify images as containing fire or not. Additionally, this classification approach was compared with the YOLOv8 classification. Onboard NVIDIA Jetson Nano, an embedded artificial intelligence computer, is used as hardware for real-time forest fire detection. Also, a ground station interface was developed to receive and display fire-related data. Thus, access to fire images and coordinate information was provided for targeted intervention in case of a fire. The UAV autonomously monitored the designated area and captured images continuously. Embedded deep learning algorithms on the Nano board enable the UAV to detect forest fires within its operational area. The detection methods produced the following results: 96% accuracy for YOLOv8 classification, 89% accuracy for YOLOv8n object detection, 96% accuracy for CNN-RCNN classification, and 89% accuracy for YOLOv5n object detection.

Literature Review: Image Processing-Based Forest Fire Detection

-Forest fires pose a significant threat to ecosystems, the environment, and human life. Early detection of forest fires is crucial for effective firefighting efforts. In this research, we propose the utilization of image processing for forest fire detection with the aim of improving rapid and accurate response in fire monitoring. Image processing methods are employed to analyze visual information obtained from satellite imagery, drones, or other sensors. The process involves image segmentation, classification, and texture analysis to identify the burned areas. Fire point detection algorithms are implemented to monitor and predict fire propagation. Through image processing, we achieved faster and more accurate forest fire detection. The information gathered from the images can be used to report real-time fire development and facilitate effective firefighting strategies. The adoption of this technology offers advantages in reducing environmental damage, economic losses, and risks to human life.

Forest Defender Fusion System for Early Detection of Forest Fires

In the past ten years, rates of forest fires around the world have increased significantly. Forest fires greatly affect the ecosystem by damaging vegetation. Forest fires are caused by several causes, including both human and natural causes. Human causes lie in intentional and irregular burning operations. Global warming is a major natural cause of forest fires. The early detection of forest fires reduces the rate of their spread to larger areas by speeding up their extinguishing with the help of equipment and materials for early detection. In this research, an early detection system for forest fires is proposed called Forest Defender Fusion. This system achieved high accuracy and long-term monitoring of the site by using the Intermediate Fusion VGG16 model and Enhanced Consumed Energy-Leach protocol (ECP-LEACH). The Intermediate Fusion VGG16 model receives RGB (red, green, blue) and IR (infrared) images from drones to detect forest fires. The Forest Defender Fusion System provides regulation of energy consumption in drones and achieves high detection accuracy so that forest fires are detected early. The detection model was trained on the FLAME 2 dataset and obtained an accuracy of 99.86%, superior to the rest of the models that track the input of RGB and IR images together. A simulation using the Python language to demonstrate the system in real time was performed.

Forest Fire Detection using UAV Imaginary Data

Forest fires are a major threat to ecosystems and human lives. Early detection of forest fires can greatly reduce their impact and detection is crucial in minimizing their damage. In recent years, the application of unmanned aerial vehicles (UAVs) with cameras with high resolution has become an effective tool for forest fire detection. However, manual detection and analysis of these images can be time-consuming and errorprone. In this study, we propose a forest fire detection method that uses Convolution Neural Networks (CNNs) to automatically analyze UAV imagery. The proposed method involves preprocessing the images to enhance the features of interest and then feeding them into the CNN for classification. We trained & tested our model using a dataset of UAV images and obtained a classification precision of 95%. The results indicate that our proposed method is effective in detecting forest fires using UAV imagery and can provide a fast and accurate means of early detection, which is critical in preventing the spread of wildfires. Forests are critical components of biosphere protection all around the planet. They provide significant contributions to the carbon cycle worldwide and support a diverse range of the animal and plant kingdoms. Fires in forests are among the most serious risks to living organisms in many parts of the world; they jeopardise the environment, including people, plants, and animals. Wildfires ravaged the regions of North Africa and the Mediterranean last year. Early detection of forest fires is critical for saving lives and property. The detection and prediction of forest fires are challenging undertakings given that wildfires begin tiny and are difficult to spo tin the distance. Fires can start small and spread swiftly to become enormous and dangerous. The combination of drones and high accuracy wildfire detection can be achieved by deep learning utilizing photos. UAVs can be used to help determine the fire’s location and the extent of its spread region, even though deep learning can be utilised to determine the fire’s qualities. This pairing is a critical building block for developing a system capable of more precise detection of wildfires. The recent published state-ofthe-art research publications on employing drones and deep learning to identify forest fires are examined in this paper.

Improving the organization of communication in the forest fire monitoring system

The article discusses the issues of early detection of forest fires. Currently, traditional monitoring methods are gradually being replaced by modern technologies based on automation and the use of artificial intelligence. The system of remote video monitoring “Lesohranitel”, which has been widely used in many regions of Russia, is considered in detail. The main advantages of this system and the basic principle of operation are considered. The scheme of communication organization of the considered system is presented, the main ways for its improvement are defined.

Early Forest Fire Detection and Control System

Early Forest Fire Detection and Control System

An AI-based Image Recognition System for Early Detection of Forest and Field Fires

Forest fires and field fires (agricultural areas, grasslands, etc.) have severe global implications, causing significant environmental and economic harm. Traditional fire detection methods often rely on human personnel, which can pose safety risks and reduce their efficiency in large-scale monitoring. There is an urgent need for real-time fire detection technology to address these challenges and minimize losses. In this research, we propose the utilization of artificial intelligence techniques, specifically Deep Learning with Convolutional Neural Networks (CNN), to tackle this issue. Our proposed system analyzes real-time images captured by IP cameras and stored on a cloud server. Its primary objective is to detect signs of fires and promptly notify users through a mobile application, ensuring timely awareness. We meticulously assembled a dataset to train our model by merging three existing datasets comprising both fire and non-fire images. Also, we incorporated images that could potentially be misinterpreted as fire, such as red trees, individuals wearing red clothing, and red flags. Furthermore, we supplemented the dataset with images of unaffected areas obtained from online sources. The final dataset consisted of 1,588 fire images and 909 non-fire images. During evaluations, our model achieved an accuracy of 93.07%. This enables effective detection, thus rapid intervention and damage reduction. It is a proactive and preventive solution to combat these devastating fires.

A lightweight early forest fire and smoke detection method

A lightweight early forest fire and smoke detection method

Early Detection of Forest and Land Fires Using Hotspots Data from S-NPP VIIRS Satellite Imagery Recording in the New Capital City Location

Forest and land fires are a natural disaster whose frequency is increasing annually, especially during dry season, forest and land fires also impact the new capital city in East Kalimantan as well. Early detection is very critical to prevent increasing impact of forest and land fires. Early detection can be done using hotspot data from satellite image recording. Based on this, it is very necessary to conduct research entitled Early Detection of Forest and Land Fires Using Hot Spots from “S-NPP VIIRS Image Records in the New Capital City Location”. The research was conducted in the new capital city location in East Kalimantan Province which consists of several sub-districts and 10 districts / cities. Descriptive quantitative method is used to analysed secondary data consist of hotspot data in each sub-district from satellite imagery data record namely S-NPP VIIRS from 2012-2022. Population number is same as the number of samples, namely hotspots number in each sub-district. Descriptive geostatistical analysis technique in ArcGIS software is used to process hotspot data. It is known that from 2012-2022, Kutai Kartanegara has the highest hotspots number with 38,694 spots, it followed by East Kutai with 36,915 spots, both are viewed from district/city scope. When viewed on the district scope, the highest was in Bengalon district from East Kutai District with 9,027 spots, it followed by Muarakaman District from Kutai Kartanagara District with 7,713 spots. The number of hotspots varies quite a lot amongst sub-district in the new capital city location, whose each have a numerous number that needs serious attention in order to minimize the loss of life and property. From analysis above, early detection is very important as a form of early warning system.

Forest Monitoring and Fire Detection System

Forest fires/Wildfires are the most common disaster in forests which lead to severe destruction of forest wealth, natural habitat and bio-diversity. Our concept is to create a system which will help in early detection of forest fire by measuring natural factors which lead to forest fire. Our Forest Monitoring and Fire Detection System uses various sensors like heat, smoke, wind, temperature and humidity to predict and detect forest fire on an early basis. The sensors are connected to Esp8266 wi-fi module which after detecting sends data to cloud. The cloud then sends the data to the website. The website displays an alert so that immediate action can be taken. Early detection and preventive measures are necessary to protect forests from fires which will be done using this system.

FuF-Det: An Early Forest Fire Detection Method under Fog

In recent years, frequent forest fires have seriously threatened the earth’s ecosystem and people’s lives and safety. With the development of machine vision and unmanned aerial vehicle (UAVs) technology, UAV monitoring combined with machine vision has become an important development trend in forest fire monitoring. In the early stages, fire shows the characteristics of a small fire target and obvious smoke. However, the presence of fog interference in the forest will reduce the accuracy of fire point location and smoke identification. Therefore, an anchor-free target detection algorithm called FuF-Det based on an encoder–decoder structure is proposed to accurately detect early fire points obscured by fog. The residual efficient channel attention block (RECAB) is designed as a decoder unit to improve the problem of the loss of fire point characteristics under fog caused by upsampling. Moreover, the attention-based adaptive fusion residual module (AAFRM) is used to self-enhance the encoder features, so that the features retain more fire point location information. Finally, coordinate attention (CA) is introduced to the detection head to make the image features correspond to the position information, and improve the accuracy of the algorithm to locate the fire point. The experimental results show that compared with eight mainstream target detection algorithms, FuF-Det has higher average precision and recall as an early forest fire detection method in fog and provides a new solution for the application of machine vision to early forest fire detection.

Drone network for early warning of forest fire and dynamic fire quenching plan generation

Wildfires are one of the most frequent natural disasters which significantly harm the environment, society, and the economy. Transfer learning algorithms and modern machine learning tools can help in early forest fire prediction, detection, and dynamic fire quenching. A group of drones that has high-definition image processing and decision-making capabilities are used to detect the forest fires in the very early stage. The proposed system generates a fire quenching plan using particle swarm optimization technique and alerts the fire and rescue department for a quick action, thereby stop the forest fire at an early stage. Also, the drone network plays a major role to track the live status of forest fire and quenches the fire. ResNet, VGGNet, MobileNet, AlexNet, and GoogLeNet are used to detect the forest fire hazards. The experimental results prove that the proposed technique GoogLeNet-TL provides 96% accuracy and 97% F1 score in comparison with the state-of-the-art deep learning models.