Traditional Fire Detection Research Articles

Deep Learning-Based Multistage Fire Detection System and Emerging Direction

Fires constitute a significant risk to public safety and property, making early and accurate detection essential for an effective response and damage mitigation. Traditional fire detection methods have limitations in terms of accuracy and adaptability, particularly in complex environments in which various fire stages (such as smoke and active flames) need to be distinguished. This study addresses the critical need for a comprehensive fire detection system capable of multistage classification, differentiating between non-fire, smoke, apartment fires, and forest fires. We propose a deep learning-based model using a customized DenseNet201 architecture that integrates various preprocessing steps and explainable AI techniques, such as Grad-CAM++ and SmoothGrad, to enhance transparency and interpretability. Our model was trained and tested on a diverse, multisource dataset, achieving an accuracy of 97%, along with high precision and recall. The comparative results demonstrate the superiority of the proposed model over other baseline models for handling multistage fire detection. This research provides a significant advancement toward more reliable, interpretable, and effective fire detection systems capable of adapting to different environments and fire types, opening new possibilities for environmentally friendly fire type detection, ultimately enhancing public safety and enabling faster, targeted emergency responses.

Fire Fighter Robot Using IOT and Mobile Application

In today's fast-paced world, fire hazards pose significant risks to life, property, and the environment. Traditional fire detection systems often rely on human intervention, leading to delays in response time and exacerbating the damage caused by fire incidents. Our project, the IoT-Based Automatic Fire Detection and Extinguisher Robot, addresses this challenge by utilizing a combination of modern sensor technology, robotics, and IoT to provide an autonomous and real-time solution. The robot is equipped with flame and temperature sensors that allow it to detect fires at an early stage. Once a fire is detected, the robot activates its movement mechanism using DC motors to navigate toward the fire while the integrated servo-controlled water pump precisely sprays water to extinguish the fire. In addition to automatic firefighting, the robot features obstacle detection using ultrasonic sensors, ensuring safe navigation in complex environments. The system includes Bluetooth and Wi-Fi communication, allowing for remote control via an Android mobile app. This app provides both manual and automatic control options and sends real-time fire alerts when the robot detects a fire. This autonomous system minimizes human intervention, speeds up response times, and reduces fire damage effectively. The robot is powered by an ESP32 microcontroller, which processes data from the sensors and controls the motors and water pump. The rescue pod feature allows the robot to perform additional operations like transporting sensitive materials during emergencies. The system's IoT functionality enables real-time alerts and remote monitoring, enhancing its capability as a scalable and cost-effective solution for residential, commercial, and industrial fire safety.

A Multi-Scale Approach to Early Fire Detection in Smart Homes

In recent years, advancements in smart home technologies have underscored the need for the development of early fire and smoke detection systems to enhance safety and security. Traditional fire detection methods relying on thermal or smoke sensors exhibit limitations in terms of response time and environmental adaptability. To address these issues, this paper introduces the multi-scale information transformer–DETR (MITI-DETR) model, which incorporates multi-scale feature extraction and transformer-based attention mechanisms, tailored specifically for fire detection in smart homes. MITI-DETR achieves a precision of 99.00%, a recall of 99.50%, and a mean average precision (mAP) of 99.00% on a custom dataset designed to reflect diverse lighting and spatial conditions in smart homes. Extensive experiments demonstrate that MITI-DETR outperforms state-of-the-art models in terms of these metrics, especially under challenging environmental conditions. This work provides a robust solution for early fire detection in smart homes, combining high accuracy with real-time deployment feasibility.

Review of Modern Forest Fire Detection Techniques: Innovations in Image Processing and Deep Learning

Fire detection and extinguishing systems are critical for safeguarding lives and minimizing property damage. These systems are especially vital in combating forest fires. In recent years, several forest fires have set records for their size, duration, and level of destruction. Traditional fire detection methods, such as smoke and heat sensors, have limitations, prompting the development of innovative approaches using advanced technologies. Utilizing image processing, computer vision, and deep learning algorithms, we can now detect fires with exceptional accuracy and respond promptly to mitigate their impact. In this article, we conduct a comprehensive review of articles from 2013 to 2023, exploring how these technologies are applied in fire detection and extinguishing. We delve into modern techniques enabling real-time analysis of the visual data captured by cameras or satellites, facilitating the detection of smoke, flames, and other fire-related cues. Furthermore, we explore the utilization of deep learning and machine learning in training intelligent algorithms to recognize fire patterns and features. Through a comprehensive examination of current research and development, this review aims to provide insights into the potential and future directions of fire detection and extinguishing using image processing, computer vision, and deep learning.

IoT-enabled fire detection for sustainable agriculture: A real-time system using flask and embedded technologies

In modern agriculture, the threat of fire poses significant economic and environmental risks. Additionally, traditional fire detection methods are inadequate and poorly integrated with advanced technologies. It is crucial to develop a more efficient and reliable fire detection system. Also, the lack of real-time and accurate monitoring in current systems compromises the resilience and sustainability of farming operations. This article details developing and implementing a real-time fire detection system tailored for smart agriculture. It integrates cutting-edge technologies, including the Internet of Things (IoT), embedded systems, and a Flask-based web application, fortified by cybersecurity measures such as login authentication and secure HTTP protocols. The system's primary aim is to monitor environmental conditions continuously in agricultural fields to detect signs of smoke or flame swiftly, facilitating preventive actions to safeguard crops and infrastructure. The architecture employs sensors distributed across fields to monitor conditions, a Raspberry Pi 3 B+ as the central processing unit for data acquisition and transmission, and a web interface developed using Flask, HTML, and CSS for secure and real-time visualization of detection data. Critical components like the MCP3208 analog-to-digital converter ensure the system's reliability and accuracy. Experimental results confirm the system's efficacy in early fire detection and real-time data visualization via the web browser. Enhanced security features, such as login authentication, protect sensitive information, while regular updates maintain data relevance. This study advances fire prevention and farm preservation efforts, offering a high-performance technological solution for proactive monitoring and quick response to fire risks, thereby supporting food security and sustainable agricultural practices.

Improving the Detectionand Warning Fire System on the Smart Campus Area using ANFIS

The adoption of IoT technology in universities has significantly improved campus security, emergency response, and control systems, particularly through IoT-based surveillance and fire detection systems. Traditional fire detection systems suffer from high false alarm rates, leading to unnecessary resource allocation. This paperproposes an intelligent fire detection system leveraging IoT and adaptive fuzzy systems to enhance accuracy and reduce false alarms.The system uses sensors to collect real-time data, which is analyzed by an Adaptive Neuro-Fuzzy Inference System (ANFIS) to determine alarm levels based on input severity. ANFIS combines fuzzy logic and neural networks, enabling learning and adaptation. Data analyzed by ANFIS is sent to ThingSpeak channels for real-time monitoring. When a fire is confirmed, alerts with fire location, timestamp, and severity are sent via SMS through GSM to the fire management system. The system utilizes cost-effective, small-sized sensors, ensuring repeatable solutions.After training, the ANFIS system achieved 99.59% accuracy, significantly reducing false alarms. It demonstrated faster detection times by reducing the fire detection rate by 28% and maintaining high detection accuracy with fewer sensors, training inputs, and epochs. Utilizing IoT and ANFIS technologies, the system integrates efficiency, speed, and reliability, protecting lives and property and highlighting the importance of safety andtechnology in serving humanity.

High-performance real-time fire detection and forecasting framework for industrial cables

In industrial scenarios, cable fires have always been the most common threat, and traditional fire detection systems often rely on a large number of sensors, and the detection range is very limited, and it is impossible to effectively predict the fire situation in time. In this paper, we propose a detection and prediction scheme for industrial cable fire, which breaks the limitations of the previous research on multi-sensor signal input, and highly couples the detection and prediction modules to realize fire prediction based on video image input only. In fire detection, we design an object detection model using HSV for flame feature enhancement based on YOLOv8, and in fire prediction aspect, we use iTransformer as a time series prediction model to mine the correlation between various parameters to predict the spread of fire. In the experiments, the average absolute percentage error of the flame detection model for the detection of flame height, width and longitudinal position was 3.49%–10.64 %, 2.45%–8.89 % and 1.61%–9.31 %, respectively, and the MAPE of the time series prediction model for the above three parameters was 11.18%–15.06 % and 4.35%–8.18 %, 3.37%–6.62 %.The results of the above experiments verify that the proposed model has the ability to quantitatively analyze the fire spread trend in the actual fire and help firefighters make decisions.

Flame detection method and application based on RGB-HSI model and initial flame growth characteristics

Abstract The safe and stable operation of hydraulic machinery equipment cannot be separated from various auxiliary electrical equipments and lubricating oils. And these are important factors in the occurrence of fire. Fire detection is used to detect fire hazards during the operation of hydraulic machinery equipment, which is of great significance to ensuring safety and stable operation of hydraulic machinery equipment and reducing economic losses. Traditional fire detection methods require the detector to be put close to the fire source, and the detector is also prone to interference, resulting in delayed detection, false alarm and missed alarm. To solve these problems, a flame detection method based on RGB-HSI model and initial flame growth characteristics is proposed in this paper. Firstly, after image processing such as binarization, filtering and dilation, the connected region of pixels is divided and the total amount of flame pixels is calculated. Finally, the region where the growth ratio of flame pixels exceeds the growth ratio threshold for three consecutive calculations is marked and alerted. The experiment proves that the real-time and accurate identification of the initial flame can be realized by combining the above image processing technology with the initial fire growth characteristics.

Unmanned Aerial Vehicle Fire Detection Platform Based on Semantic Yolov5 and Autonomous Recognition

In recent years, as fire image detection has become a research hotspot. One class of methods is color-based methods, which are very sensitive to brightness and shadows. As a result, the number of false alarms generated by these methods is high. Aiming at the task requirements of airborne binocular vision obstacle avoidance and target tracking, this paper establishes the verification platform architecture of UAV (Unmanned Aerial Vehicle) binocular vision obstacle avoidance and target tracking. For the update and maintenance of boundary regions, we can also continuously extract richer information from the boundary, make more elaborate plans, and develop an incremental method to detect locally updated maps within the boundary. The fire point can be independently and quickly identified through deep learning to extinguish the fire accurately. Assuming that the system incorrectly identifies 2 out of 80 non-fire sources as fire sources, so the results indicate a precision of about 88%, a recall of 90%. However, the traditional fire detection is around 80%.

XplAInable: Explainable AI Smoke Detection at the Edge

Wild and forest fires pose a threat to forests and thereby, in extension, to wild life and humanity. Recent history shows an increase in devastating damages caused by fires. Traditional fire detection systems, such as video surveillance, fail in the early stages of a rural forest fire. Such systems would see the fire only when the damage is immense. Novel low-power smoke detection units based on gas sensors can detect smoke fumes in the early development stages of fires. The required proximity is only achieved using a distributed network of sensors interconnected via 5G. In the context of battery-powered sensor nodes, energy efficiency becomes a key metric. Using AI classification combined with XAI enables improved confidence regarding measurements. In this work, we present both a low-power gas sensor for smoke detection and a system elaboration regarding energy-efficient communication schemes and XAI-based evaluation. We show that leveraging edge processing in a smart way combined with buffered data samples in a 5G communication network yields optimal energy efficiency and rating results.

Research on fire detection method of complex space based on multi-sensor data fusion

Efficient and reliable fire detection methods are essential for addressing complex spatial characteristics of older residential communities, which often face poor fire safety conditions, various fire types, and challenges in rescue operations. In response to the problem of lacking comprehensive analysis of multidimensional data in traditional fire detection, which often leads to false alarms and missed alarms, this paper proposes a fire detection method based on multi-sensor data fusion. Various sensors are employed, including smoke sensors, temperature sensors, CO sensors, and flame sensors. These sensors collect multidimensional data, which are fused using improved adaptive filters and fuzzy Bayesian logical reasoning algorithms to establish a fire detection model based on multi-sensor data fusion. To additionally, false alarms and missed detections caused by sensor damage are further solved by exploring hidden spaces. Experimental results demonstrate that the proposed fire detection system based on multi-sensor data fusion effectively analyses fire occurrence and progression in older residential communities, and performs stability, accuracy and real-time property.

Integration of Fire Detection and Personnel Accountability System

Fire incidents in public spaces pose significant risks to life and property. Traditional fire detection systems often suffer from delays and inaccuracies, necessitating the development of advanced solutions. This paper presents a comprehensive system for integrating fire detection and personnel accountability in surveillance scenarios. With the increasing concerns about safety in public spaces, the need for effective systems to detect fires and ensure personnel safety has become paramount. Traditional methods rely on manual intervention or basic sensors, often resulting in delayed or inaccurate alerts. To address these challenges, our system employs advanced image processing and machine learning to enhance safety measures. By analyzing surveillance camera feeds, it detects changes in pixel intensity, smoke patterns, and heat signatures to identify potential fire incidents. Machine learning models, such as convolutional neural networks (CNNs), are trained to accurately classify fire and non-fire regions in video frames. Additionally, the system tracks personnel presence and movement, ensuring accountability within the surveillance area. Modular and scalable, our system integrates seamlessly with existing infrastructure, providing reliable fire detection and personnel monitoring. Experimental results demonstrate its effectiveness, contributing to enhanced safety in public spaces and facilitating prompt emergency responses.

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.

Enhancing Fire Detection through CNN and Transfer Learning: A Comprehensive Research Study

Fire outbreaks pose significant risks to life, property, and the environment. Swift and accurate fire detection is crucial to minimize the potential damage. In recent years, advancements in computer vision techniques, particularly Convolutional Neural Networks (CNNs) and Transfer Learning, have revolutionized the field of fire detection. This article delves and introduce the comprehensive research study has taken place by the first three authors under guideline of their instructor into the integration of CNN and Transfer Learning in fire detection, highlighting their effectiveness and potential impact on enhancing fire safety measures. Fire detection plays a critical role in safeguarding lives and property. Traditional fire detection systems often rely on manual intervention and have limitations in terms of accuracy, response time, and adaptability. The advent of machine learning (ML) techniques has revolutionized fire detection by enabling the development of intelligent systems that can identify and respond to fires in real-time. This paper presents a comprehensive review of the advancements in ML-driven fire detection techniques, discusses their benefits and challenges, and outlines potential future directions for research and development.and outlines potential future directions for research and development.s of splicing for both air and water mood. The updated 92Z2 showed good result for both method

Early fire detection technique for human being using deep learning algorithm

Fire and smoke detection in today’s world is a must, especially in clustered areas where a quick response can prevent significant damages and save lives. Early detection plays a significant role in preventing the fire from spreading by alerting the emergency response personnel. It may not be possible to install traditional fire and smoke detectors everywhere. As a result, incorporating fire and smoke detection into existing closed circuit television (CCTV) systems in various places can provide a warning to the appropriate authorities, allowing for quick action to prevent the fire from spreading. This work aims in developing an early fire and smoke prediction model with CCTV footage images and video frames. The images and videos are collected from multiple datasets available online. A convolutional neural network (CNN) model is developed for early detection and prevention of the spreading of fire and compares it with transfer learning models ResNet50 and VGG19. The model obtain an accuracy of around 94% using CNN model, 95% using VGG19 and 98% using ResNet 50. A model with high accuracy can replace traditional fire detection systems which can be both cost-effective and easy to implement to existing surveillance cameras.

Fire Safety Detection Based on CAGSA-YOLO Network

The layout of a city is complex, and indoor spaces have thousands of aspects that make them susceptible to fire. If a fire breaks out, it is difficult to quell, so a fire in the city will cause great harm. However, the traditional fire detection algorithm has a low detection efficiency and high detection rate of small targets, and disasters have occurred during detection. Therefore, this paper proposes a fire safety detection algorithm based on CAGSA-YOLO and constructs a fire safety dataset to integrate common fire safety tools into fire detection, which has a preventive detection effect before a fire occurs. In the improved algorithm, the upsampling in the original YOLOv5 is replaced with the CARAFE module. By adjusting its internal Parameter contrast, the algorithm pays more attention to local regional information and obtains stronger feature maps. Secondly, a new scale detection layer is added to detect objects larger than 4 × 4. Furthermore, the sampling Ghost lightweight design replaces C3 with the C3Ghost module without reducing the mAP. Finally, a lighter SA mechanism is introduced to optimize visual information processing resources. Using the fire safety dataset, the precision, recall, and mAP of the improved model increase to 89.7%, 80.1%, and 85.1%, respectively. At the same time, the size of the improved model is reduced by 0.6 M to 13.8 M, and the Param is reduced from 7.1 M to 6.6 M.

An automatic flame detection system for outdoor areas

Traditional fire detection depends on smoke sensors. This strategy, however, is unsuited for big and open buildings, as well as outdoor regions. As a result, based on computer vision systems, this research proposes an effective method for recognizing flames in open areas. To minimize data size without losing important information, integer Haar lifting wavelet transform is used to frame and analyze the input video. Then, three color spaces (binary, hue, saturation, value (HSV), and YCbC) are used in simultaneous color detection. In binary space, Otsu’s approach is utilized to determine automated intensity pixels. Additionally, using frame differences to reduce false alarms. According to the experimental results, the approach achieves 99% accuracy for offline videos and surpasses 93% accuracy for real-time videos while maintaining a lower level of complexity.

An improved fire detection approach based on YOLO-v8 for smart cities

Fires in smart cities can have devastating consequences, causing damage to property, and endangering the lives of citizens. Traditional fire detection methods have limitations in terms of accuracy and speed, making it challenging to detect fires in real time. This paper proposes an improved fire detection approach for smart cities based on the YOLOv8 algorithm, called the smart fire detection system (SFDS), which leverages the strengths of deep learning to detect fire-specific features in real time. The SFDS approach has the potential to improve the accuracy of fire detection, reduce false alarms, and be cost-effective compared to traditional fire detection methods. It can also be extended to detect other objects of interest in smart cities, such as gas leaks or flooding. The proposed framework for a smart city consists of four primary layers: (i) Application layer, (ii) Fog layer, (iii) Cloud layer, and (iv) IoT layer. The proposed algorithm utilizes Fog and Cloud computing, along with the IoT layer, to collect and process data in real time, enabling faster response times and reducing the risk of damage to property and human life. The SFDS achieved state-of-the-art performance in terms of both precision and recall, with a high precision rate of 97.1% for all classes. The proposed approach has several potential applications, including fire safety management in public areas, forest fire monitoring, and intelligent security systems.