Fire Detection Method Research Articles

Identifying Characteristic Fire Properties with Stationary and Non-Stationary Fire Alarm Systems.

The article reviews issues associated with the operation of stationary and non-stationary electronic fire alarm systems (FASs). These systems are employed for the fire protection of selected buildings (stationary) or to monitor vast areas, e.g., forests, airports, logistics hubs, etc. (non-stationary). An FAS is operated under various environmental conditions, indoor and outdoor, favourable or unfavourable to the operation process. Therefore, an FAS has to exhibit a reliable structure in terms of power supply and operation. To this end, the paper discusses a representative FAS monitoring a facility and presents basic tactical and technical assumptions for a non-stationary system. The authors reviewed fire detection methods in terms of fire characteristic values (FCVs) impacting detector sensors. Another part of the article focuses on false alarm causes. Assumptions behind the use of unmanned aerial vehicles (UAVs) with visible-range cameras (e.g., Aviotec) and thermal imaging were presented for non-stationary FASs. The FAS operation process model was defined and a computer simulation related to its operation was conducted. Analysing the FAS operation process in the form of models and graphs, and the conducted computer simulation enabled conclusions to be drawn. They may be applied for the design, ongoing maintenance and operation of an FAS. As part of the paper, the authors conducted a reliability analysis of a selected FAS based on the original performance tests of an actual system in operation. They formulated basic technical and tactical requirements applicable to stationary and mobile FASs detecting the so-called vast fires.

Multi-Classifir Fire and Smoke Detectior Using Deep Learning

Fire is one of the most destructive powers that has been a double-edged sword. Although it is highly beneficial and provides energy when it is handled in an effective manner, it can be quite lethal if it is allowed to continue unchecked. Combustion, as well as the conversion and release of energy, is what makes fire so destructive. It is a violent process that has the potential to unleash enormous amounts of harm. This is a very unfavorable condition that has the potential to result in a truly catastrophic event. A great number of species of flora and fauna have become extinct as a result of the recent years, which have been marked by a big number of disastrous wildfires that have resulted in a large scale loss of life and property. This is one of the most deadly occurrences that has occurred in recent years. The most significant issue is that there is not yet a fire detection method that is both efficient and practical. Consequently, the purpose of this study piece is to propose an efficient multi-classifier strategy for fire detection. This approach identifies the color of the fire, the form of the fire, and the movement of the fire, in addition to the detection of smoke through the use of the convolution neural network. According to the extensive experimental results that demonstrate the superiority of the suggested multiclassifier fire and smoke detection strategy, this approach has shown to be one of the most effective approaches for fire detection. This is obvious through the fact that it has been one of the most effective techniques Key Words: — Convolution neural network, Multi-classifier, Fire shape, Fire Motion , Fore color, Temporal effect

Early fire detection technology based on improved transformers in aircraft cargo compartments

The implementation of early and accurate detection of aircraft cargo compartment fire is of great significance to ensure flight safety. The current airborne fire detection technology mostly relies on single-parameter smoke detection using infrared light. This often results in a high false alarm rate in complex air transportation environments. The traditional deep learning model struggles to effectively address the issue of long-term dependency in multivariate fire information. This paper proposes a multi-technology collaborative fire detection method based on an improved transformers model. Dual-wavelength optical sensors, flue gas analyzers, and other equipment are used to carry out multi-technology collaborative detection methods and characterize various feature dimensions of fire to improve detection accuracy. The improved Transformer model which integrates the self-attention mechanism and position encoding mechanism is applied to the problem of long-time series modeling of fire information from a global perspective, which effectively solves the problem of gradient disappearance and gradient explosion in traditional RNN (recurrent neural network) and CNN (convolutional neural network). Two different multi-head self-attention mechanisms are used to classify and model multivariate fire information, respectively, which solves the problem of confusing time series modeling and classification modeling in dealing with multivariate classification tasks by a single attention mechanism. Finally, the output results of the two models are fused through the gate mechanism. The research results show that, compared with the traditional single-feature detection technology, the multi-technology collaborative fire detection method can better capture fire information. Compared with the traditional deep learning model, the multivariate fire prediction model constructed by the improved Transformer can better detect fires, and the accuracy rate is 0.995.

Fire Detection in Urban Areas Using Multimodal Data and Federated Learning

Fire chemical sensing for indoor detection of fire plays an essential role because it can detect chemical volatiles before smoke particles, providing a faster and more reliable method for early fire detection. A thermal imaging camera and seven distinct fire-detecting sensors were used simultaneously to acquire the multimodal fire data that is the subject of this paper. The low-cost sensors typically have lower sensitivity and reliability, making it impossible for them to detect fire at greater distances. To go beyond the limitation of using solely sensors for identifying fire, the multimodal dataset is collected using a thermal camera that can detect temperature changes. The proposed pipeline uses image data from thermal cameras to train convolutional neural networks (CNNs) and their many versions. The training of sensors data (from fire sensors) uses bidirectional long-short memory (BiLSTM-Dense) and dense and long-short memory (LSTM-DenseDenseNet201), and the merging of both datasets demonstrates the performance of multimodal data. Researchers and system developers can use the dataset to create and hone cutting-edge artificial intelligence models and systems. Initial evaluation of the image dataset has shown densenet201 as the best approach with the highest validation parameters (0.99, 0.99, 0.99, and 0.08), i.e., Accuracy, Precision, Recall, and Loss, respectively. However, the sensors dataset has also shown the highest parameters with the BILSTM-Dense approach (0.95, 0.95, 0.95, 0.14). In a multimodal data approach, image and sensors deployed with a multimodal algorithm (densenet201 for image data and Bi LSTM- Dense for Sensors Data) has shown other parameters (1.0, 1.0, 1.0, 0.06). This work demonstrates that, in comparison to the conventional deep learning approach, the federated learning (FL) approach performs privacy-protected fire leakage classification without significantly sacrificing accuracy and other validation parameters.

FFYOLO: A Lightweight Forest Fire Detection Model Based on YOLOv8

Forest is an important resource for human survival, and forest fires are a serious threat to forest protection. Therefore, the early detection of fire and smoke is particularly important. Based on the manually set feature extraction method, the detection accuracy of the machine learning forest fire detection method is limited, and it is unable to deal with complex scenes. Meanwhile, most deep learning methods are difficult to deploy due to high computational costs. To address these issues, this paper proposes a lightweight forest fire detection model based on YOLOv8 (FFYOLO). Firstly, in order to better extract the features of fire and smoke, a channel prior dilatation attention module (CPDA) is proposed. Secondly, the mixed-classification detection head (MCDH), a new detection head, is designed. Furthermore, MPDIoU is introduced to enhance the regression and classification accuracy of the model. Then, in the Neck section, a lightweight GSConv module is applied to reduce parameters while maintaining model accuracy. Finally, the knowledge distillation strategy is used during training stage to enhance the generalization ability of the model and reduce the false detection. Experimental outcomes demonstrate that, in comparison to the original model, FFYOLO realizes an mAP0.5 of 88.8% on a custom forest fire dataset, which is 3.4% better than the original model, with 25.3% lower parameters and 9.3% higher frames per second (FPS).

Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model

Forest fires present a significant challenge to ecosystems, particularly due to factors like tree cover that complicate fire detection tasks. While fire detection technologies, like YOLO, are widely used in forest protection, capturing diverse and complex flame features remains challenging. Therefore, we propose an enhanced YOLOv8 multiscale forest fire detection method. This involves adjusting the network structure and integrating Deformable Convolution and SCConv modules to better adapt to forest fire complexities. Additionally, we introduce the Coordinate Attention mechanism in the Detection module to more effectively capture feature information and enhance model accuracy. We adopt the WIoU v3 loss function and implement a dynamically non-monotonic mechanism to optimize gradient allocation strategies. Our experimental results demonstrate that our model achieves a mAP of 90.02%, approximately 5.9% higher than the baseline YOLOv8 network. This method significantly improves forest fire detection accuracy, reduces False Positive rates, and demonstrates excellent applicability in real forest fire scenarios.

Detection and management of coal seam outcrop fire in China: a case study

The outcrop fire area in Rujigou Coal Mine in Ningxia, China has been burning continuously for over 100 years. This not only results in wastage of resources but also poses significant damage to the ecological environment. Previous research on open fire detection has mainly focused on coalfield fire areas, using single method such as infrared remote sensing or surface temperature measurement, magnetic method, electrical method, radon measurement and mercurimetry. However, the outcrop fire area has migrated to deeper parts over the years, conventional single fire zone detection methods are not capable of accurately detecting the extent of the fire zone, inversion interpretation is faced with the problem of many solutions. In fire management, current research focuses on the development of new materials, such as fly ash gel, sodium silicate gel, etc., However, it is often difficult to quickly extinguish outcrop fire areas with a single technique. Considering this status quo, unmanned aerial vehicle (UAV) infrared thermal imaging was employed to initially detect the scope of the outcrop fire area, and then both the spontaneous potential and directional drilling methods were adopted for further scope detection in pursuit of more accurate results. In addition, an applicable fire prevention and extinguishing system was constructed, in which three-phase foam was injected for the purpose of absorbing heat and cooling. Furthermore, the composite colloid was used to plug air leakage channels, and loess was backfilled to avoid re-combustion. The comprehensive detection and control technologies proposed in this study can be applied to eliminating the outcrop fire area and protecting the environment. This study can provide guidance and reference for the treatment of other outcrop fire areas.

CNTCB-YOLOv7: An Effective Forest Fire Detection Model Based on ConvNeXtV2 and CBAM

In the context of large-scale fire areas and complex forest environments, the task of identifying the subtle features and aspects of fire can pose a significant challenge for the deep learning model. As a result, to enhance the model’s ability to represent features and its precision in detection, this study initially introduces ConvNeXtV2 and Conv2Former to the You Only Look Once version 7 (YOLOv7) algorithm, separately, and then compares the results with the original YOLOv7 algorithm through experiments. After comprehensive comparison, the proposed ConvNeXtV2-YOLOv7 based on ConvNeXtV2 exhibits a superior performance in detecting forest fires. Additionally, in order to further focus the network on the crucial information in the task of detecting forest fires and minimize irrelevant background interference, the efficient layer aggregation network (ELAN) structure in the backbone network is enhanced by adding four attention mechanisms: the normalization-based attention module (NAM), simple attention mechanism (SimAM), global attention mechanism (GAM), and convolutional block attention module (CBAM). The experimental results, which demonstrate the suitability of ELAN combined with the CBAM module for forest fire detection, lead to the proposal of a new method for forest fire detection called CNTCB-YOLOv7. The CNTCB-YOLOv7 algorithm outperforms the YOLOv7 algorithm, with an increase in accuracy of 2.39%, recall rate of 0.73%, and average precision (AP) of 1.14%.

Real-time forest fire detection, monitoring, and alert system using Arduino

<div align="center"><span>Early fire detection is critical to protecting forests from wildfires and enabling rapid responses to minimize fire spread. Existing forest fire detection methods cannot quickly detect forest fires and evaluate the fire risk of these sensitive areas. Hence, this research aims to develop a real-time forest fire detection, monitoring, and alert system. The development of the</span><span> system started with assembling temperature and humidity sensors, a smoke sensor, an Arduino microcontroller, and a wireless fidelity module. Then, a fire monitoring and alert system was developed using Blynk. From the sensitivity flame sensor analysis with the fire, the flame sensor detected the <span>presence of fire up to 60 cm. The sensor also indicated high temperature (45 °C)</span> and low humidity (53.4%) at noon. Low temperature (29 ℃) and high humidity (88.4%) were identified in the morning. Moreover, the highest carbon dioxide (CO<sub>2</sub>) concentration of 1,800 ppm was recorded when the smoke from the fire was detected. The global positioning system module shows the accurate real-time location of the system displayed in the Blynk application. In conclusion, this system can detect and monitor early forest fires in real-time and can alert the authorities to protect forests from wildfires.</span></div>

Fire-PPYOLOE: An Efficient Forest Fire Detector for Real-Time Wild Forest Fire Monitoring

Forest fire has the characteristics of sudden and destructive, which threatens safety of people’s life and property. 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. However, the actual forest environment is complex and diverse, and the vision image is affected by various factors easily such as geographical location, seasons, cloudy weather, day and night, etc. In this paper, we propose a novel fire detection method called Fire-PPYOLOE. We design a new backbone and neck structure leveraging large kernel convolution to capture a large arrange area of reception field based on the existing fast and accurate object detection model PP-YOLOE. In addition, our model maintains the high-speed performance of the single-stage detection model and reduces model parameters by using CSPNet significantly. Extensive experiments are conducted to show the effectiveness of Fire-PPYOLOE from the views of detection accuracy and speed. The results show that our Fire-PPYOLOE is able to detect the smoke- and flame-like objects because it can learn features around the object to be detected. It can provide real-time forest fire prevention and early detection.

Refined fire detection and band selection method in hyperspectral remote sensing imagery based on sparse-VIT

Hyperspectral remote sensing has powerful ability to perceive information of land surface, and can be applied to the field of fire monitoring to achieve refined fire analysis. In this paper, we study the fire detection method based on hyperspectral remote sensing, design an end to end fire detection model based on sparse visual transformer, and propose a sensitive band selection method under the transformer framework. In order to suppress the interference of invalid bands in hyperspectral data, the sparse attention mechanism and the top-k selection mechanism are introduced in the model to zeroing the attention score of invalid bands. To achieve dimension reduction, a non-maximum attention suppression algorithm under the transformer framework is designed and band pruning is further introduced to eliminate invalid bands and redundant bands. The model adopts input mode of band-exclusive-token, which makes the pruning operation equivalent to band selection. We publish a hyperspectral fire detection dataset and the performance of our proposed model is verified on the dataset.

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.

Deep Learning-Based Fire Detection for Enhanced Safety Systems

Fire detection systems are a critical aspect of modern safety and security systems, playing a pivotal role in safeguarding lives and property against the destructive force of fires. Rapid and accurate identification of fire incidents is essential for timely response and mitigation efforts. Traditional fire detection methods have made substantial advancements, but with the advent of computer vision technologies, the field has witnessed a transformative shift. This paper presents a method for fire detection using deep convolutional neural network (CNN) models. This approach used transfer learning by employing two pre-trained CNN models from the ImageNet dataset: VGG (Visual Geometry Group) and InceptionV3 to extract valuable features from input images. Then, these extracted features serve as input for a machine learning (ML) classifier, namely the Softmax classifier. The Softmax activation function computes the probability distribution to assign accurate class probabilities for discriminating between two types of images: fire and non-fire. Experimental results showed that the proposed method successfully detected fire areas and achieved seamless classification performance compared to other current fire detection methods.

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.

An Image-Based Fire Monitoring Algorithm Resistant to Fire-like Objects

Due to its wide monitoring range and low cost, visual-based fire detection technology is commonly used for fire detection in open spaces. However, traditional fire detection algorithms have limitations in terms of accuracy and speed, making it challenging to detect fires in real time. These algorithms have poor anti-interference ability against fire-like objects, such as emissions from factory chimneys, clouds, etc. In this study, we developed a fire detection approach based on an improved YOLOv5 algorithm and a fire detection dataset with fire-like objects. We added three Convolutional Block Attention Modules (CBAMs) to the head network of YOLOv5 to improve its feature extraction ability. Meanwhile, we used the C2f module to replace the original C2 module to capture rich gradient flow information. Our experimental results show that the proposed algorithm achieved a mAP@50 of 82.36% for fire detection. In addition, we also conducted a comparison test between datasets with and without labeling information for fire-like objects. Our results show that labeling information significantly reduced the false-positive detection proportion of fire-like objects incorrectly detected as fire objects. Our experimental results show that the CBAM and C2f modules enhanced the network’s feature extraction ability to differentiate fire objects from fire-like objects. Hence, our approach has the potential to improve fire detection accuracy, reduce false alarms, and be more cost-effective than traditional fire detection methods. This method can be applied to camera monitoring systems for automatic fire detection with resistance to fire-like objects.

A lightweight early forest fire and smoke detection method

A lightweight early forest fire and smoke detection method

Identifying and managing disturbance-stimulated flammability in woody ecosystems.

Many forest types globally have been subject to an increase in the frequency of, and area burnt by, high-severity wildfire. Here we explore the role that previous disturbance has played in increasing the extent and severity of subsequent forest fires. We summarise evidence documenting and explaining the mechanisms underpinning a pulse of flammability that may follow disturbances such as fire, logging, clearing or windthrow (a process we term disturbance-stimulated flammability). Disturbance sometimes initiates a short initial period of low flammability, but then drives an extended period of increased flammability as vegetation regrows. Our analysis initially focuses on well-documented cases in Australia, but we also discuss where these pattens may apply elsewhere, including in the Northern Hemisphere. We outline the mechanisms by which disturbance drives flammability through disrupting the ecological controls that limit it in undisturbed forests. We then develop and test a conceptual model to aid prediction of woody vegetation communities where such patterns of disturbance-stimulated flammability may occur. We discuss the interaction of ecological controls with climate change, which is driving larger and more severe fires. We also explore the current state of knowledge around the point where disturbed, fire-prone stands are sufficiently widespread in landscapes that they may promote spatial contagion of high-severity wildfire that overwhelms any reduction in fire spread offered by less-flammable stands. We discuss how land managers might deal with the major challenges that changes in landscape cover and altered fire regimes may have created. This is especially pertinent in landscapes now dominated by extensive areas of young forest regenerating after logging, regrowing following broadscale fire including prescribed burning, or regenerating following agricultural land abandonment. Where disturbance is found to stimulate flammability, then key management actions should consider the long-term benefits of: (i) limiting disturbance-based management like logging or burning that creates young forests and triggers understorey development; (ii) protecting young forests from disturbances and assisting them to transition to an older, less-flammable state; and (iii) reinforcing the fire-inhibitory properties of older, less-flammable stands through methods for rapid fire detection and suppression.

An Indoor Fire Detection Method Based on Multi-Sensor Fusion and a Lightweight Convolutional Neural Network.

Indoor fires pose significant threats in terms of casualties and economic losses globally. Thus, it is vital to accurately detect indoor fires at an early stage. To improve the accuracy of indoor fire detection for the resource-constrained embedded platform, an indoor fire detection method based on multi-sensor fusion and a lightweight convolutional neural network (CNN) is proposed. Firstly, the Savitzky-Golay (SG) filter is used to clean the three types of heterogeneous sensor data, then the cleaned sensor data are transformed by means of the Gramian Angular Field (GAF) method into matrices, which are finally integrated into a three-dimensional matrix. This preprocessing stage will preserve temporal dependency and enlarge the characteristics of time-series data. Therefore, we could reduce the number of blocks, channels and layers in the network, leading to a lightweight CNN for indoor fire detection. Furthermore, we use the Fire Dynamic Simulator (FDS) to simulate data for the training stage, enhancing the robustness of the network. The fire detection performance of the proposed method is verified through an experiment. It was found that the proposed method achieved an impressive accuracy of 99.1%, while the number of CNN parameters and the amount of computation is still small, which is more suitable for the resource-constrained embedded platform of an indoor fire detection system.

A Survey: Fire Detection Alarm System Using OpenCV

The Fire Detection Alarm System using OpenCV is a computer vision-based solution designed to provide early detection of fires in real-time video streams. Fires pose substantial threats to both human safety and property, underscoring the importance of timely intervention. This system leverages OpenCV, a powerful computer vision library, to identify flames and smoke patterns in video frames, triggering alerts upon detection. So, we propose a smoke and fire detection method based on YOLOv5 network as a solution to address this problem.

Fire identification based on improved multi feature fusion of YCbCr and regional growth

Fire is one of the mutable hazards that damage properties and destroy forests. However, fire-like objects easily influence many existing image-based fire detection methods. In order to improve the performance of fire identification, this paper proposes a new fire identification algorithm by merging fire segmentation and multi feature fusion of fire. First, the improved YCbCr models in the reflection and non-reflection environment are constructed according to the color model. Simultaneously, the reflection and non-reflection conditions can be judged according to the segmented area. Second, the seed points are determined according to the weighted average of centroid of each connected region. Simultaneously, the fine segmentation of fire image is implemented according to the entropy of average contrast and uniformity within the connected region. Experiments show that the segmentation is not affected by image noises. Finally, the quantitative indicators of fire identification are given according to the coefficient of variation of area, the dispersion of centroid and the circularity. Cases show that the proposed identification method of fire not only accurately identifies fire, but also has a lower computation complexity than the deep learning method.