Smoke Detection Research Articles

Design and implementation of a smoke alarm system using YOLOv8

Abstract. This paper present a smoke alarm system built using Yolov8, a state-of-the-art object detection model. The system is designed to detect smoke in real-time environments and alert users in the event of a fire or smoke detection. The use of Yolov8 in this system offers several advantages over other object detection models, making it a suitable choice for building an effective smoke alarm system. One of the critical advantages of Yolov8 is its high accuracy in detecting objects with a small size, such as smoke. This is achieved through its efficient and robust architecture, which allows it to process images quickly and accurately. In comparison to other object detection models, Yolov8 demonstrates superior performance in terms of speed and accuracy. Furthermore, Yolov8s ability to detect objects with high precision and low false positives is crucial for a smoke alarm system. This is because smoke detection needs high accuracy to prevent false alarms and promptly detect real threats. The smoke alarm system built using Yolov8 can be implemented in various environments, including homes, offices, and public buildings. It can provide early warnings for fires and smoke, enabling timely evacuation and reducing potential injuries or damages. The implementation of this system can greatly contribute to improving safety in various environments by providing early warnings of fires and smoke.

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.

FireNet: A Lightweight and Efficient Multi-Scenario Fire Object Detector

Fire and smoke detection technologies face challenges in complex and dynamic environments. Traditional detectors are vulnerable to background noise, lighting changes, and similar objects (e.g., clouds, steam, dust), leading to high false alarm rates. Additionally, they struggle with detecting small objects, limiting their effectiveness in early fire warnings and rapid responses. As real-time monitoring demands grow, traditional methods often fall short in smart city and drone applications. To address these issues, we propose FireNet, integrating a simplified Vision Transformer (RepViT) to enhance global feature learning while reducing computational overhead. Dynamic snake convolution (DSConv) captures fine boundary details of flames and smoke, especially in complex curved edges. A lightweight decoupled detection head optimizes classification and localization, ideal for high inter-class similarity and small targets. FireNet outperforms YOLOv8 on the Fire Scene dataset (FSD) with a mAP@0.5 of 80.2%, recall of 78.4%, and precision of 82.6%, with an inference time of 26.7 ms. It also excels on the FSD dataset, addressing current fire detection challenges.

Experimental Study based on Case Analysis for Efficient Management of Smoke Detectors

This paper presents an experimental study aimed at developing effective management strategies for smoke detectors under various environmental conditions. Through surveys and expert consultations, the primary causes of unwanted fire alarms were identified, and scenarios involving humidity, cooking smoke, and dust were selected for re-enactment experiments. The study investigated the operational changes in smoke detectors under varying humidity levels and conducted tests under both normal and humid indoor conditions for cooking smoke. Additional experiments were performed on smoke detectors coated with dust to observe their performance under different humidity and smoke conditions. As a result, it was found that the response time of smoke detectors varied depending on the level of moisture present, the amount of smoke generated, and the accumulation of dust. In conclusion, the study experimentally confirmed that smoke detectors should not be installed in areas exposed to high humidity and highlighted the importance of regular inspection and cleaning to minimize unwanted fire alarms.

Sensors for Smoking Detection in Epidemiological Research: Scoping Review.

The use of wearable sensors is being explored as a challenging way to accurately identify smoking behaviors by measuring physiological and environmental factors in real-life settings. Although they hold potential benefits for aiding smoking cessation, no single wearable device currently achieves high accuracy in detecting smoking events. Furthermore, it is crucial to emphasize that this area of study is dynamic and requires ongoing updates. This scoping review aims to map the scientific literature for identifying the main sensors developed or used for tobacco smoke detection, with a specific focus on wearable sensors, as well as describe their key features and categorize them by type. According to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) protocol, an electronic search was conducted on the PubMed, MEDLINE, and Web of Science databases, using the following keywords: ("biosensors" OR "biosensor" OR "sensors" OR "sensor" OR "wearable") AND ("smoking" OR "smoke"). Among a total of 37 studies included in this scoping review published between 2012 and March 2024, 16 described sensors based on wearable bands, 15 described multisensory systems, and 6 described other strategies to detect tobacco smoke exposure. Included studies provided details about the design or application of wearable sensors based on an elastic band to detect different aspects of tobacco smoke exposure (eg, arm, wrist, and finger movements, and lighting events). Some studies proposed a system composed of different sensor modalities (eg, Personal Automatic Cigarette Tracker [PACT], PACT 2.0, and AutoSense). Our scoping review has revealed both the obstacles and opportunities linked to wearable devices, offering valuable insights for future research initiatives. Tackling the recognized challenges and delving into potential avenues for enhancement could elevate wearable devices into even more effective tools for aiding smoking cessation. In this context, continuous research is essential to fine-tune and optimize these devices, guaranteeing their practicality and reliability in real-world applications.

Real-Time Smoke Detection in Surveillance Videos Using an Enhanced RT-DETR Framework with Triplet Attention and HS-FPN

This study addresses the urgent need for an efficient and accurate smoke detection system to enhance safety measures in fire monitoring, industrial safety, and urban surveillance. Given the complexity of detecting smoke in diverse environments and under real-time constraints, our research aims to solve challenges related to low-resolution imagery, limited computational resources, and environmental variability. This study introduces a novel smoke detection system that utilizes the real-time detection Transformer (RT-DETR) architecture to enhance the speed and precision of video analysis. Our system integrates advanced modules, including triplet attention, ADown, and a high-level screening-feature fusion pyramid network (HS-FPN), to address challenges related to low-resolution imagery, real-time processing constraints, and environmental variability. The triplet attention mechanism is essential for detecting subtle smoke features, often overlooked due to their nuanced nature. The ADown module significantly reduces computational complexity, enabling real-time operation on devices with limited resources. Furthermore, the HS-FPN enhances the system’s robustness by amalgamating multi-scale features for reliable detection across various smoke types and sizes. Evaluation using a diverse dataset showcased notable improvements in average precision (AP50) and frames per second (FPS) metrics compared to existing state-of-the-art networks. Ablation studies validated the contributions of each component in achieving an optimal balance between accuracy and operational efficiency. The RT-DETR-based smoke detection system not only meets real-time requirements for applications like fire monitoring, industrial safety, and urban surveillance but also establishes a new performance benchmark in this field.

Transforming University Library Building Conservation: A Descriptive Study of Preservation Practices in Nigeria

The study examined the conservation of library building for effective information preservation among university libraries in Enugu State, Nigeria. Descriptive survey with population of 325 library staff made up the study. The instruments for collecting data is questionnaire, analysed and presented in mean score, standard deviation as well as percentage and t-test. Results: conservation criteria for library building are clearing and cleaning of the library building, repair of roof, windows and doors, installation of fire and smoke alarm system, and the provision of enough security personnel around the building. Library buildings are being conserved to protect resources against natural disasters like flooding, and earthquake, against man-made disaster like fire, making library resources accessible, promote an end to wasteful use of non-renewable information resources, to encourages best practices in records management, and it provides an extremely attractive environment for the use of books. Findings on the adequacy level of policy and regulatory framework shows that disaster management, storage capacity for documentary resources, appropriateness of environmental conditions are to be consider in the library. The study recommended that the university library management should provide monitoring system for environmental condition, storage capacity for digital resources and disaster preventive measure in the library.

FGYOLO: An Integrated Feature Enhancement Lightweight Unmanned Aerial Vehicle Forest Fire Detection Framework Based on YOLOv8n

To address the challenges of complex backgrounds and small, easily confused fire and smoke targets in Unmanned Aerial Vehicle (UAV)-based forest fire detection, we propose an improved forest smoke and fire detection algorithm based on YOLOv8. Considering the limited computational resources of UAVs and the lightweight property of YOLOv8n, the original model of YOLOv8n is improved, the Bottleneck module is reconstructed using Group Shuffle Convolution (GSConv), and the residual structure is improved, thereby enhancing the model’s detection capability while reducing network parameters. The GBFPN module is proposed to optimize the neck layer network structure and fusion method, enabling the more effective extraction and fusion of pyrotechnic features. Recognizing the difficulty in capturing the prominent characteristics of fire and smoke in a complex, tree-heavy environment, we implemented the BiFormer attention mechanism to boost the model’s ability to acquire multi-scale properties while retaining fine-grained features. Additionally, the Inner-MPDIoU loss function is implemented to replace the original CIoU loss function, thereby improving the model’s capacity for detecting small targets. The experimental results of the customized G-Fire dataset reveal that FGYOLO achieves a 3.3% improvement in mean Average Precision (mAP), reaching 98.8%, while reducing the number of parameters by 26.4% compared to the original YOLOv8n.

Wildfire Smoke Detection Enhanced by Image Augmentation with StyleGAN2-ADA for YOLOv8 and RT-DETR Models

Wildfire Smoke Detection Enhanced by Image Augmentation with StyleGAN2-ADA for YOLOv8 and RT-DETR Models

Optimization of Smoke, Flame, and Heat Detector Maintenance on MV Pan Mutiara

Smoke, flame, and heat detectors are included in the active fire protection system that must be present on board. The function of these detectors is to detect fires early. This research aims to optimize the maintenance of smoke, flame, and heat detectors on board ships. This research was conducted using a qualitative method with primary data sources obtained through notes from interviews, field observations, documentation, and secondary data obtained from various literacies. This research was conducted at MV Pan Mutiara. In this study, it can be concluded that the optimization of smoke, flame, and heat detector maintenance needs to be done on MV Pan Mutiara so that the detector can work properly because if the detector is damaged, this equipment loses its function and it can be a leading cause of fire that cannot be controlled. Then the maintenance optimization is carried out by making a special schedule, as evidenced by the results of the interview, where according to 3RD Officer the maintenance schedule is made by dividing the total number of detectors on board over 6 months, this period is taken into consideration of the workload of 3RD Officer at POS SM company which is more compared to other companies.

Mcan-YOLO: An Improved Forest Fire and Smoke Detection Model Based on YOLOv7

Forest fires pose a significant threat to forest resources and wildlife. To balance accuracy and parameter efficiency in forest fire detection, this study proposes an improved model, Mcan-YOLO, based on YOLOv7. In the Neck section, the asymptotic feature pyramid network (AFPN) was employed to effectively capture multi-scale information, replacing the traditional module. Additionally, the content-aware reassembly of features (CARAFE) replaced the conventional upsampling method, further reducing the number of parameters. The normalization-based attention module (NAM) was integrated after the ELAN-T module to enhance the recognition of various fire smoke features, and the Mish activation function was used to optimize model convergence. A real fire smoke dataset was constructed using the mean structural similarity (MSSIM) algorithm for model training and validation. The experimental results showed that, compared to YOLOv7-tiny, Mcan-YOLO improved precision by 4.6%, recall by 6.5%, and mAP50 by 4.7%, while reducing the number of parameters by 5%. Compared with other mainstream algorithms, Mcan-YOLO achieved better precision with fewer parameters.

Flame and smoke detection based on channel shuffling and adaptive spatial feature fusion

Flame and smoke detection based on channel shuffling and adaptive spatial feature fusion

Automated system for classifying images of video streams for timely detection of fires

ABSTRACT Early detection of a fire during its development stage is highly desired to minimize human and material losses. The improvement of the efficiency of these monitoring systems through the analysis of fire video data drawn from unmanned aerial vehicles is considered . Classification of video images when monitoring a fire event on the territory is used based on their segmentation into identical rectangular segments of a given size and then assigning them to one of three classes: Smoke, Flame, and Indifferent classes. The Walsh-Hadamard transform was used to form descriptors for the Weak classifiers. Descriptors were calculated for three Weak classifiers; first, the Walsh-Hadamard transform was calculated for the window of the entire segment, and its spectral coefficients were used for the first Weak classifier. Next, the descriptors were calculated in windows whose sizes were half and a quarter of the original window size. The classifier consisted of three independently-trained neural networks. A simple ensemble averaging block was used to combine the outputs of those networks. A special software code has been developed to classify video images. The code allows us to create a database of segments of the ‘Smoke’ and ‘Flame’ classes, determine the two-dimensional Walsh-Hadamard spectrum of video image segments, train fully connected neural networks, conduct exploratory analysis and evaluate the relevance of the calculated two-dimensional spectral coefficients. For validation we used real data from Сlosed Circuit Television cameras presented in open databases. The experimental studies on the classification of video data containing flames and smoke showed that the proposed method has an average smoke detection accuracy of 86% and an average flame detection accuracy of 89.5%. Errors II in smoke detection and flame detection had the averages of 13% and 4.5%, respectively.

The Fire-ViT Model for Tunnel Fire Detection with Vision Transformer Improvement

Smoke detection in tunnels presents unique challenges, including low light conditions, visual obstructions like smoke and headlights, and the need to analyze ultrahigh-resolution images. To address these challenges, this study introduces an innovative model named Fire-ViT, leveraging the vision transformer (ViT) architecture. Unlike traditional convolutional neural networks that often struggle with false positives under these complex conditions, Fire-ViT incorporates an attention mechanism and multiperceptron layer, significantly enhancing its capability to discern details in high-resolution images specific to tunnel environments. The model’s performance is outstanding, achieving an accuracy rate of 99.87% on a high-resolution tunnel image dataset, markedly surpassing that of conventional models. Notably, Fire-ViT not only elevates detection accuracy and robustness but also cuts training time in half. This efficiency, coupled with its adaptability to the intricate tunnel environment, makes Fire-ViT an ideal solution for early warning systems against fires in tunnels, fulfilling the demand for high-standard, fine-grained fire detection.

DCNFYOLO: Dual-Convolution Network and Feature Fusion for High-Precision Smoke Detection

Fast, real-time, and accurate detection of smoke characteristics in the early stage of a fire is crucial for reducing fire losses. Existing smoke detection methods mainly rely on traditional algorithms and smoke sensors, and these approaches have limitations in false detection rates, accuracy, and real-time performance. Therefore, a novel DCNFYOLO network for smoke detection is proposed in this paper. Firstly, Switchable Atrous Convolution (SAConv) is introduced in the YOLOv5 backbone network to enhance the fusion extraction of smoke features by the Convolutional Neural Network (CNN). Secondly, both Distribution Shifts Convolution (DSConv) operator and Efficient Channel Attention (ECA) mechanisms are considered in the neck part to reduce the computational load of the model, and better capture the relationship between channels to improve the detection performance. Finally, to make low-quality examples less harmful to the gradients, the Wise-IoU (WIoU) loss function in the prediction part is used for reducing the competitiveness of high-quality anchor frames during model training, allowing the model to converge more quickly and stably. Experimental results show that the DCNFYOLO network can achieve a remarkable detection accuracy of 96.6%, which has a substantial improvement of 7.7% compared with the original YOLOv5 network performance, thereby validating the effectiveness of the proposed network.

Using computer vision to classify, locate and segment fire behavior in UAS-captured images

The widely adaptable capabilities of artificial intelligence, in particular deep learning and computer vision have led to significant research output regarding flame and smoke detection. The composition of flame and smoke, also described as fire behavior, can be considerably different depending on factors like weather, fuels, and the specific landscape fire is being observed on. The ability to detect definable classes of fire behavior using computer vision has not been explored and could be helpful given it often dictates how firefighters respond to fire situations. To test whether types of fire behavior could be reliably classified, we collected and labeled a unique unmanned aerial system (UAS) image dataset of fire behavior classifications to be trained and validated using You Only Look Once (YOLO) detection models. Our 960 labeled images were sourced from over 21 h of UAS video collected during prescribed fire operations covering a large region of Texas and Louisiana, United States. National Wildfire Coordinating Group (NWCG) fire behavior observations and descriptions served as a reference for determining fire behavior classes during labeling. YOLOv8 models were trained on NWCG Rank 1–3 fire behavior descriptions in grassland, shrubland, forested, and combined fire regimes within our study area. Models were first trained and validated on classifying isolated image objects of fire behavior, and then separately trained to locate and segment fire behavior classifications in UAS images. Models trained to classify isolated image objects of fire behavior consistently performed at a mAP of 0.808 or higher, with combined fire regimes producing the best results (mAP = 0.897). Most segmentation models performed relatively poorly, except for the forest regime model at a box (locate) and mask (segment) mAP of 0.59 and 0.611, respectively. Our results indicate that classifying fire behavior with computer vision is possible in different fire regimes and fuel models, whereas locating and segmenting fire behavior types around background information is relatively difficult. However, it may be a manageable task with enough data, and when models are developed for a specific fire regime. With an increasing number of destructive wildfires and new challenges confronting fire managers, identifying how new technologies can quickly assess wildfire situations can assist wildfire responder awareness. Our conclusion is that levels of abstraction deeper than just detection of smoke or flame are possible using computer vision and could make even more detailed aerial fire monitoring possible using a UAS.

Numerical study on smoke temperature distribution in naturally ventilated inclined tunnels: Effects of tunnel width and tunnel slope

Inclined tunnel fires exhibit unique characteristics compared to horizontal tunnel fires as a result of the stack effect. Nevertheless, there is still a lack of consensus among previous scholars regarding the smoke temperature distribution in inclined tunnel fires. This work conducts a numerical investigation on the smoke back-layering length upstream of the fire source and the temperature decay downstream of the fire source in naturally ventilated inclined tunnels with varying widths and slopes. Results indicate that as the tunnel slope increases from 0° to 8°, the smoke back-layering length first decreases and then continues at a constant value of 1.47H4/3/W1/3, while the effect of tunnel width is negligible. The decay factor in the one-dimensional flow stage (x/H≥4) exhibits two distinct patterns related to tunnel width: it initially remains constant and then drops when the tunnel is wide, whereas it monotonically drops when the tunnel is narrow. Two empirical models are developed to identify the segmented characteristics and verified through the relevant data from existing literature with similar fire scenarios. This work offers a method for estimating the smoke temperature distribution in inclined tunnels, providing crucial insights for fire prevention, smoke detection, and safety strategies in tunnel engineering.

Smoke Detection and Recognition Based on Flame Feature Extraction

The sensor-based fire monitoring scheme requires the installation of temperature and smoke detectors near the monitoring target. Meanwhile, the pictorial fire detection technology is capable of capturing the location and development degree of the fire instantly, enabling rapid detection. Aiming at the issue that the traditional fire monitoring system is prone to failure in large-space indoor and open outdoor environments, a digital image fire monitoring algorithm based on the characteristics of fire flames and smoke is proposed. In the early stage of a fire, smoke is frequently accompanied by its generation and can spread over a large area in the space and is not easily blocked. Therefore, the detection of smoke can realize the early warning of fires and conduct fire rescue work in a targeted manner. In this paper, the background subtraction method is initially employed to extract the moving object, and then the required flame model features are extracted through image preprocessing, and the fire image is identified through multi-feature fusion. The experiment indicates that the flame identification method proposed in this paper can accurately identify the flame under various circumstances and provides a favorable judgment basis for fire alarms.

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.

A Review on Fire and Smoke Detection With Intelligent Control for Enhanced Safety Using Machine Learning (ML) and Internet of Things (IoT)

A Review on Fire and Smoke Detection With Intelligent Control for Enhanced Safety Using Machine Learning (ML) and Internet of Things (IoT)