Fire Detection System Research Articles

IMPLEMENTASI SISTEM TANGGAP DARURAT KEBAKARAN PADA GEDUNG UTAMA DI INDUSTRI PUPUK

A fire emergency response system is a series of procedures that must be carried out when a fire occurs to protect the safety of building occupants. The main objective of this final project is to ensure that the fire emergency response system in the main building of in the fertilizer industry. can function well when a fire occurs and can protect the safety of all main building employees. This research uses a qualitative research method which uses two types of data, namely primary data with in-depth interviews and observations, and secondary data from document reviews, company archives and literature studies. The results and discussion based on variables related to the implementation of the fire emergency response system can be concluded that the informants implemented the fire emergency response system procedures well and in accordance with established regulations. This is proven based on observations that in the main building of PT.X already has a fire emergency response system, such as a fire detection and alarm system, fire extinguishing system, evacuation routes, gathering points and other supporting equipment. Apart from that, there are also evacuation procedures, training and simulations, as well as maintenance of a fire emergency response system that is complete and in accordance with established regulations.

IoT-Enabled Fire Detection and Alert System Leveraging HSV Thresholding

Fire detection plays an important role in minimizing damage caused by forest fires and enhancing response times. In this proposed study, a real-time fire detection system using a USB camera and Raspberry Pi, has been developed utilizing the concepts of IoT and image processing. The system continuously monitors the environment for the presence of fire by analysing video frames captured by the camera. The HSV colour space model is utilized to detect fire-like colours based on predefined thresholds, ensuring accurate identification of fire regions. Upon detection, the system triggers an alarm, retrieves geolocation data, and sends real-time alerts to a designated webpage, providing the fire’s exact location and a notification for immediate action. This approach eliminates the need for manual intervention, making it suitable for deployment in high-risk areas. The system’s modular design allows for integration with additional technologies such as automated fire extinguishing systems or GSM-based notifications, expanding its potential applications in real-world scenarios.

Agri-Tech Innovations for Sustainability: A Fire Detection System Based on MQTT Broker and IoT to Improve Environmental Risk Management

Agri-Tech Innovations for Sustainability: A Fire Detection System Based on MQTT Broker and IoT to Improve Environmental Risk Management

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.

Automated Fire Sensing and Fire Extinguisher Module

The Hazard of fire and fire explosions poses significant risks to both property and human safety, necessitating immediate action to minimize damage. Conventional fire detection methods that rely on human observation may result in delays, particularly in unattended areas. To address these challenges, a specialized fire detection and extinguishing system is being developed with the capability to autonomously detect and extinguish fires. This system will also promptly notify individuals through messages and phone calls upon fire detection, improving response times and ensuring immediate awareness of fire incidents. Equipped with Arduino microcontrollers, the system integrates advanced sensors for precise detection of heat and smoke, allowing accurate pinpointing of fire locations. The use of Arduino technology provides adaptable functionality in various environments, ranging from residential to commercial settings. Effective communication of fire alerts is essential for promptly notifying firefighters and occupants, facilitating timely intervention and safe evacuations. The objective is to create an efficient system that harnesses modern technology to enhance fire safety measures, ultimately reducing fire related risks and effectively protecting lives.

Revolutionizing Wildfire Detection Through UAV-Driven Fire Monitoring with a Transformer-Based Approach

The rapid detection and accurate localization of wildfires are critical for effective disaster management and response. This study proposes an innovative Unmanned aerial vehicles (UAVs)-based fire detection system leveraging a modified Miti-DETR model tailored to meet the computational constraints of drones. The enhanced architecture incorporates a redesigned AlexNet backbone with residual depthwise separable convolution blocks, significantly reducing computational load while improving feature extraction and accuracy. Furthermore, a novel residual self-attention mechanism addresses convergence issues in transformer networks, ensuring robust feature representation for complex aerial imagery. The model, which was trained on the FLAME dataset encompassing diverse fire scenarios, demonstrates superior performance in terms of Mean Average Precision (mAP) and Intersection over Union (IoU) metrics compared to existing systems. Its capability to detect and localize fires across varied backgrounds highlights its practical application in real-world scenarios. This advancement represents a pivotal step forward in applying deep learning for real-time wildfire detection, with implications for broader emergency management applications.

Optimizing Fire Scene Analysis: Hybrid Convolutional Neural Network Model Leveraging Multiscale Feature and Attention Mechanisms

The rapid and accurate detection of fire scenes in various environments is crucial for effective disaster management and mitigation. Fire scene classification is a critical aspect of modern fire detection systems that directly affects public safety and property preservation. This research introduced a novel hybrid deep learning model designed to enhance the accuracy and efficiency of fire scene classification across diverse environments. The proposed model integrates advanced convolutional neural networks with multiscale feature extraction, attention mechanisms, and ensemble learning to achieve superior performance in real-time fire detection. By leveraging the strengths of pre-trained networks such as ResNet50, VGG16, and EfficientNet-B3, the model captures detailed features at multiple scales, ensuring robust detection capabilities. Including spatial and channel attention mechanisms further refines the focus on critical areas within the input images, reducing false positives and improving detection precision. Extensive experiments on a comprehensive dataset encompassing wildfires, building fires, vehicle fires, and non-fire scenes demonstrate that the proposed framework outperforms existing cutting-edge techniques. The model also exhibited reduced computational complexity and enhanced inference speed, making it suitable for deployment in real-time applications on various hardware platforms. This study sets a new benchmark for fire detection and offers a powerful tool for early warning systems and emergency response initiatives.

Micro-Incubator Protocol for Testing a CO2 Sensor for Early Warning of Spontaneous Combustion

A protocol for detecting the potential occurrence of spontaneous combustion (SC) in stored cottonseeds and peanuts using a micro-incubator is described. The protocol indicates how to quantify CO2 production rates and final CO2 levels in wet versus dry cottonseed and peanut samples, which can provide crucial data for the early detection of SC risk in storage facilities. The experimental design utilizes a micro-incubator to simulate conditions found in large bulk crop storage. Parameters monitored include CO2 concentration, temperature, and relative humidity. The protocol includes preparation methods, experimental procedures for both control (dry) and wet seed tests, and test termination criteria that allow for safe experimentation of likely pathogenic fungi. The protocol has three replicates for wet and dry conditions. The protocol is intended to facilitate future experimental studies and ultimately contribute to the development of a consistently reliable early warning fire detection system for SC in cottonseed and peanut warehouse facilities. A consistently reliable fire detection system would address a critical need in the cotton and peanut industry for improved fire risk management and insurability of storage facilities.

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.

Automated image-based fire detection and alarm system using edge computing and cloud-based platform

To tackle the increasing wildfire challenges, this paper presents an automated image-based fire detection and alarm system utilizing edge computing and a cloud-based platform, specifically designed for urban building fire detection. The system captures both RGB and infrared images from thermal cameras and employs existing computer vision techniques to detect fire characteristics such as flames and smoke. By integrating edge computing, the system minimizes latency to enhance the accuracy of fire detection and alarm activation. The cloud platform supports extensive data storage, analysis, and remote monitoring, which can ensure data accessibility and scalable data management. The proposed system descriptions include a detailed system architecture design, data collection, and the selection and application of detection algorithms that leverage both RGB and thermal image data for fire detection. Using the campus building and surrounding risk-prone areas as a testbed, the proposed system demonstrated desired fire detection capabilities and a robust solution to quickly identify and respond to fire incidents within the urban area. The proposed system functionalities can be scaled and adapted to other fire risk-prone areas.

Development of Smart Fire Detection System for Household Kitchen

Development of Smart Fire Detection System for Household Kitchen

임베디드 플랫폼을 위한 화재 조기 감지 시스템: 오경보 최소화를 위한 딥러닝 접근 방식

임베디드 플랫폼을 위한 화재 조기 감지 시스템: 오경보 최소화를 위한 딥러닝 접근 방식

An Explainable AI-Based Modified YOLOv8 Model for Efficient Fire Detection

Early fire detection is the key to saving lives and limiting property damage. Advanced technology can detect fires in high-risk zones with minimal human presence before they escalate beyond control. This study focuses on providing a more advanced model structure based on the YOLOv8 architecture to enhance early recognition of fire. Although YOLOv8 is excellent at real-time object detection, it can still be better adjusted to the nuances of fire detection. We achieved this advancement by incorporating an additional context-to-flow layer, enabling the YOLOv8 model to more effectively capture both local and global contextual information. The context-to-flow layer enhances the model’s ability to recognize complex patterns like smoke and flames, leading to more effective feature extraction. This extra layer helps the model better detect fires and smoke by improving its ability to focus on fine-grained details and minor variation, which is crucial in challenging environments with low visibility, dynamic fire behavior, and complex backgrounds. Our proposed model achieved a 2.9% greater precision rate, 4.7% more recall rate, and 4% more F1-score in comparison to the YOLOv8 default model. This study discovered that the architecture modification increases information flow and improves fire detection at all fire sizes, from tiny sparks to massive flames. We also included explainable AI strategies to explain the model’s decision-making, thus adding more transparency and improving trust in its predictions. Ultimately, this enhanced system demonstrates remarkable efficacy and accuracy, which allows additional improvements in autonomous fire detection systems.

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.

FireDA: A Domain Adaptation-Based Method for Forest Fire Recognition with Limited Labeled Scenarios

Vision-based forest fire detection systems have significantly advanced through Deep Learning (DL) applications. However, DL-based models typically require large-scale labeled datasets for effective training, where the quality of data annotation is crucial to their performance. To address challenges related to the quality and quantity of labeling, a domain adaptation-based approach called FireDA is proposed for forest fire recognition in scenarios with limited labels. Domain adaptation, a subfield of transfer learning, facilitates the transfer of knowledge from a labeled source domain to an unlabeled target domain. The construction of the source domain FBD is initiated, which includes three common fire scenarios: forest (F), brightness (B), and darkness (D), utilizing publicly available labeled data. Subsequently, a novel algorithm called Neighborhood Aggregation-based 2-Stage Domain Adaptation (NA2SDA) is proposed. This method integrates feature distribution alignment with target domain Proxy Classification Loss (PCL), leveraging a neighborhood aggregation mechanism and a memory bank designed for the unlabeled samples in the target domain. This mechanism calibrates the source classifier and generates more accurate pseudo-labels for the unlabeled sample. Consequently, based on these pseudo-labels, the Local Maximum Mean Discrepancy (LMMD) and the Proxy Classification Loss (PCL) are computed. To validate the efficacy of the proposed method, the publicly available forest fire dataset, FLAME, is employed as the target domain for constructing a transfer learning task. The results demonstrate that our method achieves performance comparable to the supervised Convolutional Neural Network (CNN)-based state-of-the-art (SOTA) method, without requiring access to labels from the FLAME training set. Therefore, our study presents a viable solution for forest fire recognition in scenarios with limited labeling and establishes a high-accuracy benchmark for future research.

Real-Time Fire Classification Models Based on Deep Learning for Building an Intelligent Multi-Sensor System

Fire detection systems are critical for mitigating the damage caused by fires, which can result in significant annual property losses and fatalities. This paper presents a deep learning-based fire classification model for an intelligent multi-sensor system aimed at early and reliable fire detection. The model processes data from multiple sensors that detect various parameters, such as temperature, humidity, and gas concentrations. Several deep learning architectures were evaluated, including LSTM, GRU, Bi-LSTM, LSTM-FCN, InceptionTime, and Transformer. The models were trained on data collected from controlled fire scenarios and validated for classification accuracy, loss, and real-time performance. The results indicated that the LSTM-based models (particularly Bi-LSTM and LSTM) could achieve high classification accuracy and low false alarm rates, demonstrating their effectiveness for real-time fire detection. The findings highlight the potential of advanced deep-learning models to enhance the reliability of sensor-based fire detection systems.

Fabric-based intelligent fire-warning and flame-retardant coating: A review of advances, challenges and prospects

Fabric-based fire-warning and flame-retardant coatings could achieve dual functions of active warning response and passive fire retardancy. In contrast to conventional fire detection systems that necessitate installation at designated locations, the contact surface between the coating sensing material and the flame is substantially augmented. Therefore, it possesses crucial application value in enhancing the warning efficiency and fire safety performance of flammable substrates. This review primarily delved into the flame retardancy and fire warning of fabric coating materials. It thoroughly examined the current research status of fabric-based flame-retardant coatings and analyzed the fire-warning mechanisms of voltage-based and resistance-based coatings. Furthermore, this comprehensive review elucidated the research and current applications of various coatings based on sensing materials such as graphene oxide, carbon nanotubes, and MXene in the fire warning and flame resistance. Additionally, it is worth noting that the current functionality of fabric-based coatings is relatively limited, necessitating the incorporation of multi-functionality to adapt to various usage scenarios. In addition, it is necessary to develop large-scale coating preparation technique to achieve commercial production and combine artificial intelligence technology to achieve higher-level fire warning and flame-resistant application. Finally, the future development trend of the fabric-based coating was prospected.

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.

A hierarchical k-out-of-n optimization model for enhancing reliability of fire alarm systems

Fire alarm systems are crucial for mitigating the risks and damages associated with fires in process industries. These systems heavily depend on the performance of fire detection sensors to provide early detection and warnings, facilitating prompt evacuation and intervention measures. In order to improve the reliability of sensor systems typically requires the use of redundancy allocation techniques, such as a k-out-of-n configuration. In k-out-of-n configuration, the signals from all sensors are directed to a voter, which compares them and transmits a final signal once at least k signals are matched. However, challenges arise due to diverse failure modes with distinct impacts on the system. Integrating sensors through a single-layer k-out-of-n configuration, when there is only one voter, fails to fully highlight the potential advantages of redundancy in sensor systems. This paper addresses the limitations of the conventional configurations by introducing a hierarchical k-out-of-n system in which sensors can be integrated across multiple layers. In the k-out-of-n hierarchical system, sensors are organized into distinct groups, each employing its own k-out-of-n sub-system. Subsequently, the outputs of these sensor groups can be integrated with other groups through their respective voters in higher layers of the hierarchy. Ultimately, at the top layer, all the voters from the preceding layers must be connected to a top voter to transmit a final signal. Furthermore, an optimization model is developed to minimize the system cost while maximizing reliability, considering the best selection of sensors and the system configuration. The proposed optimization model takes into account a multi-sensor system with two competing failure modes and incorporates the probability of sensors' failure modes in the system. The hierarchical k-out-of-n system provides an opportunity to validate sensors by comparing them within their designated groups, allowing for the identification of the location of a failure and the planning of appropriate actions accordingly. The proposed optimization model is applied to a case of a fire detection system in storage warehouse to demonstrate its advantages over the conventional model.