Here are a few use cases for this project:

1. Fire Emergency Identification: The model can be used in fire detection systems in public and private buildings. When it identifies fire or smoke and the presence of a human, it could trigger alarms and deploy necessary measures such as spraying fire retardant or auto-dialing emergency services.

2. Personal Safety Applications: In smart home systems, the model could provide real-time alerts to homeowners if fire or smoke is detected, especially if there's a human present, indicating potential danger.

3. Forest Fire Surveillance: The model can analyze drone or satellite imagery to identify forest fires and detect if anyone is trapped or injured within the vicinity, helping to strategize the response.

4. Industrial Safety: The model can be used in industries, particularly those with higher fire risk like oil and gas, chemical, and manufacturing, to monitor for fire or smoke and ensure the safety of the workers.

5. Disaster Response Training: The model can be used in simulations to train emergency response teams. For instance, the model would identify fire, smoke, and humans in various scenarios, providing realistic training opportunities for firefighters and rescue teams.

This dataset contains 11027 labeled images for the detection of fire and smoke instances in diverse real-world scenarios. The annotations are provided in YOLO format with bounding boxes and class labels for two classes: fire and smoke. The dataset is divided into an 80% training set with 10,090 fire instances and 9724 smoke instances, a 10% Validation set with 1,255 fire and 1,241 smoke instances, and a 10% Test set with 1,255 fire and 1,241 smoke instances. This dataset is suitable for training and evaluating fire and smoke detection models, such as YOLOv8, YOLOv9, and similar deep learning-based frameworks in the context of emergency response, wildfire monitoring, and smart surveillance.

Fire And Smoke Detection( Fog)

## Overview

Fire And Smoke Detection( Fog) is a dataset for object detection tasks - it contains Fire And Smoke Fog annotations for 12,499 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset is collected by DataCluster Labs, India. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai

This dataset is an extremely challenging set of over 7000+ original Fire and Smoke images captured and crowdsourced from over 400+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster.

Dataset Features

Dataset size : 7000+ Captured by : Over 1000+ crowdsource contributors Resolution : 98% images HD and above (1920x1080 and above) Location : Captured with 400+ cities accross India Diversity : Various lighting conditions like day, night, varied distances, view points etc. Device used : Captured using mobile phones in 2020-2021 Usage : Fire and Smoke detection, Smart cameras, Fire and Smoke alarming system, etc.

Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record

*To download full datasets or to submit a request for your dataset needs, please drop a mail on sales@datacluster.ai . Visit www.datacluster.ai to know more.

Fire And Smoke Detection 2

## Overview

Fire And Smoke Detection 2 is a dataset for object detection tasks - it contains Fire Smoke annotations for 5,512 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Combined Forest Fire Datasets

This dataset is a compilation of forest fire-related datasets sourced from various repositories and research platforms. It combines data from multiple sources to provide a comprehensive collection for analysis and research purposes. The datasets included in this compilation are:

1. Dataset 1 (gjmr63rz2r-1): Retrieved from Mendeley
2. Dataset 2 (archive): Retrieved from Kaggle
3. Dataset 3 (ForestFireDataset(Object Detection)): Retrieved from Mendeley
4. Dataset 4 (D-Fire): Retrieved from GitHub
5. Dataset 5 (ForestFireDataset(Classifications)): Retrieved from Mendeley
6. Dataset 6 (mivia-fire): Retrieved from Mivia
7. Dataset 7 (mivia-smoke): Retrieved from Mivia

The datasets cover various aspects of forest fires, including object detection, classifications, and related imagery. By combining these datasets, researchers and analysts can access a diverse range of data for studying forest fires, developing predictive models, and exploring mitigation strategies.

Copyright Notice:

The individual datasets included in this compilation retain their respective copyrights. Users are encouraged to refer to the original sources for specific usage terms and citation requirements. This combined dataset is provided for research and educational purposes only.

Forest Fire And Smoke Detection Using UAV Imaging_2

## Overview

Forest Fire And Smoke Detection Using UAV Imaging_2 is a dataset for object detection tasks - it contains Fire annotations for 1,151 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Ensuring safety and safeguarding indoor properties require reliable fire detection methods. Traditional detection techniques that use smoke, heat, or fire sensors often fail due to false positives and slow response time. Existing deep learning-based object detectors fall short of improved accuracy in indoor settings and real-time tracking, considering the dynamic nature of fire and smoke. This study aimed to address these challenges in fire and smoke detection in indoor settings. It presents a hyperparameter-optimized YOLOv5 (HPO-YOLOv5) model optimized by a genetic algorithm. To cover all prospective scenarios, we created a novel dataset comprising indoor fire and smoke images. There are 5,000 images in the dataset, split into training, validation, and testing samples at a ratio of 80:10:10. It also used the Grad-CAM technique to provide visual explanations for model predictions, ensuring interpretability and transparency. This research combined YOLOv5 with DeepSORT (which uses deep learning features to improve the tracking of objects over time) to provide real-time monitoring of fire progression. Thus, it allows for the notification of actual fire hazards. With a mean average precision (mAP@0.5) of 92.1%, the HPO-YOLOv5 model outperformed state-of-the-art models, including Faster R-CNN, YOLOv5, YOLOv7 and YOLOv8. The proposed model achieved a 2.4% improvement in mAP@0.5 over the original YOLOv5 baseline model. The research has laid the foundation for future developments in fire hazard detection technology, a system that is dependable and effective in indoor scenarios.

1) Data Introduction • The Sensor-Fusion Smoke Detection Classification dataset is data related to smoke detectors that have reduced the number of fire victims.Fire can be prevented by the prediction results of smoke detectors.

2) Data Utilization (1) Sensor-Fusion Smoke Detection Classification data has characteristics that: • The dataset includes factors such as temperature, humidity, TVOC, CO2, H2 and Air Pressure. (2) Sensor-Fusion Smoke Detection Classification data can be used to: • Machine learning research: Help devise machine learning models that detect smoke and generate fire alarms with the help of IoT data.

Fire & Smoke Detection

## Overview

Fire & Smoke Detection is a dataset for object detection tasks - it contains Fire Smoke annotations for 1,912 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

The FIRESENSE database, developed within the FP7-ENV-244088
''FIRESENSE - Fire Detection and Management through a Multi-Sensor Network for
the Protection of Cultural Heritage Areas from the Risk of Fire and Extreme
Weather" project contains videos for testing flame and smoke detection algorithms.

Specifically:

a) for flame detection 11 positive and 16 negative videos are provided, while

b) for smoke detection, 13 positive and 9 negative videos are provided.

Results using this database are presented in many papers, including:

1. K. Dimitropoulos, P. Barmpoutis, N. Grammalidis, "Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection", IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Vol. 25, No. 2, February 2015, pp. 339-351.

Ensuring safety and safeguarding indoor properties require reliable fire detection methods. Traditional detection techniques that use smoke, heat, or fire sensors often fail due to false positives and slow response time. Existing deep learning-based object detectors fall short of improved accuracy in indoor settings and real-time tracking, considering the dynamic nature of fire and smoke. This study aimed to address these challenges in fire and smoke detection in indoor settings. It presents a hyperparameter-optimized YOLOv5 (HPO-YOLOv5) model optimized by a genetic algorithm. To cover all prospective scenarios, we created a novel dataset comprising indoor fire and smoke images. There are 5,000 images in the dataset, split into training, validation, and testing samples at a ratio of 80:10:10. It also used the Grad-CAM technique to provide visual explanations for model predictions, ensuring interpretability and transparency. This research combined YOLOv5 with DeepSORT (which uses deep learning features to improve the tracking of objects over time) to provide real-time monitoring of fire progression. Thus, it allows for the notification of actual fire hazards. With a mean average precision (mAP@0.5) of 92.1%, the HPO-YOLOv5 model outperformed state-of-the-art models, including Faster R-CNN, YOLOv5, YOLOv7 and YOLOv8. The proposed model achieved a 2.4% improvement in mAP@0.5 over the original YOLOv5 baseline model. The research has laid the foundation for future developments in fire hazard detection technology, a system that is dependable and effective in indoor scenarios.

Dataset

This dataset was created by cubeai

Contents

FLAME is a fire image dataset collected by drones during a prescribed burning piled detritus in an Arizona pine forest. The dataset includes video recordings and thermal heatmaps captured by infrared cameras. The captured videos and images are annotated and labeled frame-wise to help researchers easily apply their fire detection and modeling algorithms.

1) Data Introduction • The Forest Fire Dataset is an image classification dataset consisting of images related to wildfires and smoke. It is designed to serve as visual training material for the development of fire and smoke detection algorithms. The dataset includes two classification labels: 'fire' for wildfire images and 'smoke' for smoke-related images.

2) Data Utilization (1) Characteristics of the Forest Fire Dataset: • The dataset contains images of fires and smoke captured in various environments, making it suitable for the development of early detection and classification systems. • Most of the images are sourced from the wildfire detection dataset released by the University of Science and Technology of China (USTC), and they contain a wide range of visual features reflecting real wildfire scenarios.

(2) Applications of the Forest Fire Dataset: • Development of wildfire and smoke recognition AI models: Can be used to train image-based artificial intelligence models that automatically classify the presence of fire or smoke. • Experiments for disaster response system development: Useful as foundational data for building technologies such as forest surveillance, CCTV video analysis, and real-time alert systems. • Environmental research and climate change applications: Can be used to analyze wildfire occurrence patterns and assess the effectiveness of fire detection algorithms under climate change scenarios.

fire and smoke

The reflected beam smoke detector market, currently valued at $417 million in 2025, is projected to experience robust growth, driven by increasing safety regulations in large public spaces and a rising awareness of fire safety across various sectors. The 6.5% CAGR indicates a significant expansion over the forecast period (2025-2033), reaching an estimated market size exceeding $700 million by 2033. Key drivers include the stringent fire safety codes mandated for commercial buildings (such as warehouses, conference centers, airports, and hotels), necessitating advanced smoke detection systems. Furthermore, the growing adoption of intelligent building management systems and the increasing preference for reliable, early-warning fire detection technologies are contributing to market expansion. The market segmentation reveals a significant demand for adjustable sensitivity detectors offering flexible deployment across diverse environments, compared to unadjustable sensitivity models. Geographic distribution showcases a strong presence in North America and Europe, with significant growth potential in the Asia-Pacific region, driven by rapid urbanization and infrastructure development. Technological advancements, including the integration of IoT capabilities and improved detection accuracy, are major trends shaping the market. However, the high initial investment cost associated with installing these systems, especially in large facilities, and the potential for false alarms in certain environments could act as restraints. Nevertheless, ongoing innovation and the long-term benefits of preventing devastating fire incidents will likely overcome these challenges. Competitive dynamics are characterized by a mix of established players like Honeywell, Bosch, and Eaton, alongside specialized manufacturers focusing on niche applications. This dynamic competitive landscape fosters innovation and drives down costs, benefiting end-users. The market will continue to grow through ongoing technological improvements, expansion into emerging markets, and strengthened safety regulations globally.

A Fire and Smoke Dataset is a collection of images and data specifically curated for the development, training, and evaluation of machine learning models and computer vision algorithms designed to detect and classify fires and smoke in various environments..

The global high-sensitivity smoke detection system market is experiencing robust growth, driven by stringent safety regulations across various sectors and increasing awareness of fire safety. The market, estimated at $1.5 billion in 2025, is projected to expand significantly over the forecast period (2025-2033), fueled by a compound annual growth rate (CAGR) of approximately 7%. Several factors contribute to this growth. The escalating adoption of advanced fire detection technologies in commercial buildings (e.g., large shopping malls, hospitals, and data centers) is a major driver, demanding systems with superior sensitivity to detect even minute amounts of smoke, minimizing property damage and loss of life. The residential sector also contributes significantly, propelled by rising disposable incomes and a growing preference for enhanced home security features. Furthermore, the industrial sector's need for robust fire safety protocols in manufacturing facilities and warehouses fuels market expansion. Technological advancements, such as the development of more sophisticated ionization and photoelectric sensors with improved sensitivity and faster response times, are further propelling market growth. Different sensor types cater to varying needs; ionization sensors are preferred for detecting fast-flaming fires, while photoelectric sensors are better suited for detecting smoldering fires. Market segmentation reveals a strong preference for high-sensitivity systems in commercial applications, followed by the industrial and residential sectors. While the ionization and photoelectric types currently dominate, other innovative technologies are emerging, offering enhanced features such as early warning capabilities and integration with smart building management systems. Geographic distribution shows strong market penetration in North America and Europe, attributed to high levels of safety awareness and robust infrastructure. However, Asia-Pacific is expected to witness substantial growth due to rapid urbanization and industrialization in countries like China and India. While there are restraints such as high initial investment costs for advanced systems and the potential for false alarms, the overall market outlook remains positive, driven by ongoing technological innovation and increased regulatory pressure. The leading companies are actively investing in R&D to develop more sophisticated and reliable systems, further consolidating their market presence.

Snapshot img

Smoke Detector Market Size 2024-2028

The smoke detector market size is forecast to increase by USD 2.47 billion at a CAGR of 15.2% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. The increase in residential construction is driving market demand, as new buildings require the installation of smoke detection systems for safety reasons. Another trend is the integration of smoke detectors with building systems and fire protection systems, enabling advanced features such as remote monitoring,and automatic alerts.
Additionally, the disposal of outdated smoke detectors is creating opportunities for market growth, as consumers and businesses replace old systems with more advanced and reliable options such as wireless fire detection system. These trends, along with others, are shaping the future of the market.

What will be the Size of the Smoke Detector Market During the Forecast Period?

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The market is experiencing significant growth due to heightened safety and security concerns in both residential and commercial spaces. With the increasing number of fatal accidents caused by fire, the demand for enhanced smoke detection systems and fire and gas detection systems is on the rise. Traditional smoke detectors, while effective, are prone to false alarms, leading to consumer dissatisfaction.
To address this issue, the market is witnessing an amalgamation of IoT-enabled devices, smartphones, and wearables, enabling instant action and real-time risk monitoring. These advanced systems utilize a combination of photoelectric and ionization sensors, ensuring comprehensive coverage. Building codes mandate the use of battery-powered smoke detectors, driving the demand for long-lasting batteries.
Wi-Fi and Bluetooth connectivity options provide ease of installation and maintenance. Despite these advancements, the challenge of minimizing false alarms remains a key focus area for market participants.

How is this Smoke Detector Industry segmented and which is the largest segment?

The smoke detector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Commercial and public
  Residential
  Industrial


Type

  Photoelectric
  Dual sensor
  Ionization
  Others


Geography

  North America

    Canada
    US


  Europe

    Germany


  APAC

    China
    India


  South America



  Middle East and Africa

By End-user Insights

The commercial and public segment is estimated to witness significant growth during the forecast period.

Photoelectric smoke detectors employ light sources and sensors In their sensing chambers to identify smoke. Smoke particles obstruct the light beam and reflect it onto the sensors, triggering the alarm. These detectors are particularly effective in identifying smoldering fires, which release smoke and fumes before progressing into open flames. Photoelectric smoke detectors are available in both battery-powered and hardwired versions. Battery-powered models necessitate more frequent maintenance due to their reliance on disposable batteries. In contrast, hardwired smoke detectors are directly connected to a building's electrical systems, eliminating the need for battery replacements. Adherence to building codes is crucial in both residential and commercial spaces to ensure safety from deadly gases, including carbon monoxide.

Smart smoke detectors, IoT-enabled devices, and smartphone-connected models are increasingly popular, offering enhanced security and early detection features. The market for smoke detectors is growing in developed nations, driven by the commercial realty sector, retail centers, and industrial applications. Regulations and standardized building codes mandate the installation of fire safety equipment, including smoke detectors and portable fire extinguishers, on ceilings, exterior walls, and mounting surfaces. Proper installation is essential to minimize false alarms and ensure effective risk monitoring. Maintenance services are available for addressing potential issues and ensuring the longevity of the devices.

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The commercial and public segment was valued at USD 772.90 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 33% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American market leads the glo