A Survey: Fire Detection Alarm System Using OpenCV

Abstract

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

Introduction

I. INTRODUCTION

A fire detection alarm system using OpenCV, or Open-Source Computer Vision Library, represents a groundbreaking approach to fire safety and prevention. OpenCV, which harnesses the power of computer vision, enables the system to analyse live video feeds or images to detect the presence of flames or smoke. Upon detection, the system triggers alarms, alerts, or other safety protocols to swiftly respond to the threat, potentially preventing disasters and safeguarding lives and property. This innovative fire detection alarm system represents a significant advancement in fire safety, offering real-time monitoring and rapid response capabilities that traditional systems simply cannot match.

A fire detection alarm system utilizing OpenCV is an innovative and critical application of computer vision technology in ensuring fire safety. OpenCV, an open-source computer vision library, is employed to analyse real-time image and video data, enabling the system to detect the presence of fire and smoke.

By monitoring visual cues such as color, motion, and patterns within the video feed, this system can provide early and accurate fire detection, triggering timely alarms and potentially averting catastrophic consequences. It is a cost-effective and efficient approach to fire detection that complements traditional fire alarms and can be integrated into various environments, from homes and offices to industrial facilities, enhancing overall fire safety and response.

A fire detection alarm system leveraging OpenCV represents a critical advancement in modern safety technology. OpenCV, a powerful computer vision library, equips such systems with the ability to detect fires and smoke in real-time through image and video analysis.

By analyzing visual cues, such as color, motion, and heat patterns, OpenCV-based fire detection systems can swiftly identify potential fire incidents.

This technology not only enables early fire detection but also aids in pinpointing the exact location of the fire. In doing so, it offers a robust and proactive means of fire prevention and response, reducing the potential for damage, injuries, and loss of life. Fire detection alarm systems using OpenCV exemplify the synergy between cutting-edge technology and safety, forging a path towards a more secure and protected future.

II. PROBLEM STATEMENT

Design an efficient fire detection alarm system utilizing OpenCV. The system should process real-time video streams or images, employing computer vision techniques to identify fire patterns and anomalies. Upon detection,

the system must trigger an audible and visual alarm to alert users about the potential fire hazard. The solution should be accurate, capable of distinguishing between actual fires and false positives, while also considering factors such as varying lighting conditions and camera perspectives.

III. OBJECTIVES

1. The primary objective of the Fire Detection Alarm System using OpenCV is to develop a robust and accurate fire detection solution that utilizes computer vision techniques.
2. To identify instances of fire, smoke, and flames in real-time video streams.
3. The system aims to provide early warning of potential fire incidents, enabling swift and effective responses to minimize damage and ensure the safety of individuals and property.

IV. LITERATURE REVIEW

A literature survey for a fire detection alarm system using OpenCV would involve reviewing research papers, articles, and projects related to fire detection methods using computer vision and OpenCV.

Zahraa Shihab Al Hakeem, Hawaraa H.Abass, Haider Ismael Shahadi have described automatic approach for detecting fire over a video feed. Decrease fire detection time by less than 0.25 secs. The three authors of the paper entitled as An Automatic System for Detection of Fires in Outdoor Areas [1] contributed in the detection of fire mechanism and its causes by Zahraa Shihab Al Hakeem, Hawaraa H.Abass, Haider Ismael Shahadi.

Shuyan Liu, Jianbin Fing have described this paper proposes a high accuracy, high detection rate, and high-speed smoke and fire detection method to address the current challenges of smoke and fire detection in industrial applications. Shuyan Liu, Jianbin Fing have described fire detection and smoke detection mechanism in Real time Smoke and fire warning detection method based on an improved YOLOv5 model paper [2].

Shuyan Liu, Jianbin Feng described in A Real-Time Smoke and Fire Warning Detection Method Based on an Improved YOLOv5 Model [2] that —Smoke and fire detection technology has been very mature with the development of deep learning. The paper introduces an enhanced version of the YOLOv5 (You Only Look Once) model, a state-of-the-art deep learning architecture for object detection.

This enhanced model is tailored for the specific task of detecting fires and smoke. The primary focus of the paper is on achieving real-time fire and smoke detection. It emphasizes the importance of swiftly identifying these hazards to enable rapid responses in emergency situations.

G. Sathyakala and V. Kirthika have proposed in the paper entitled as “Computer Vision Based Fire Detection with a Video Alert System” [3].

The paper underscores the fundamental concept of using computer vision techniques to identify fires and smoke. It discusses how computer vision algorithms can analyze image and video data in real-time to detect visual cues associated with fire, such as color, motion, and flickering patterns.

Mr.Abdul Rahman.A, T. Bharath Kumar, G. Radha have proposed the Proposed System for Fire Detection based on the video Processing [4]. The paper emphasizes the utilization of video processing techniques to detect the presence of fires in real-time. It elaborates on the advantages of video data in capturing dynamic fire-related visual cues such as flames, smoke, and sudden changes in pixel intensity.

Bibek Shrestha, proposed a paper named as “Fire Detection Using Image Processing” in Metropolia University of Applied science [5]. The paper emphasizes the use of image processing as the foundation for fire detection. It discusses the benefits of analyzing static images to identify visual cues associated with fires, such as flames, smoke, and hotspots. The paper delves into the methods used to extract relevant visual features from images. This includes the analysis of color, texture, shape, and intensity, which are crucial for distinguishing fire-related patterns.

Aman Kumar, Flavia D Gonsalves, proposed a paper “Computer Vision Based Fire Detection System Using OpenCV - A Case Study” [6].

The paper highlights the integration of computer vision, specifically using OpenCV, as a core technology for fire detection. It discusses how this integration harnesses the power of image and video analysis. It discusses the process of collecting image and video data from the case study environment, explaining the sources, sensors, and frequency of data capture.

V. SYSTEM ARCHITECTURE

A fire detection alarm system using OpenCV in machine learning typically consists of several components and follows a specific system architecture. Here's an overview of the key components and their functions:

1. Image/Video Input: The system starts by capturing images or video frames from one or multiple cameras. These cameras can be strategically placed in areas where fire detection is required.
2. Image Preprocessing: Before feeding the images to the machine learning model, various preprocessing steps are applied. This includes resizing, colour correction, noise reduction, and enhancing image quality. OpenCV is often used for these preprocessing tasks.
3. Fire Detection Model: Machine learning models, usually based on convolutional neural networks (CNNs), are employed to detect fires in the pre-processed images. OpenCV can be used to interface with these models and process the images.
4. Training Data: The fire detection model is trained on a large dataset of images containing examples of both fire and non-fire scenarios. This dataset is crucial for the model to learn and distinguish between the two.
5. Inference and Classification: In real-time, the system uses the trained model to classify the images from the cameras. If a fire is detected, an alarm is triggered.
6. Alarm System: When a fire is detected, an alarm is activated. This could be a loud siren, flashing lights, or an alert sent to a monitoring station. OpenCV can be used to trigger these alarms and provide visual feedback.
7. User Interface: A user interface can be included to allow users to monitor the system, review alerts, and configure settings. OpenCV can also be used to display the camera feed and highlight areas where fire is detected.
8. Alerts and Notifications: In addition to triggering local alarms, the system may send alerts and notifications to relevant parties, such as building occupants, security personnel, or emergency services.
9. Logging and Storage: The system can log detected events, including images or video clips of the fire, for later review or analysis.
10. Maintenance and Updates: Regular maintenance and updates are necessary to ensure the system's effectiveness. This includes retraining the machine learning model with new data and updating the software.

It's important to note that while OpenCV is commonly used for image preprocessing and interfacing with machine learning models, the actual machine learning model used for fire detection may vary. The choice of the model will depend on the specific requirements of the system, such as real-time processing, accuracy, and hardware constraints. Additionally, the effectiveness of such a system depends on the quality of the training data and the robustness of the algorithms used.

VI. PROPOSED OUTCOMES

The proposed outcomes of a fire detection alarm system using OpenCV can be summarized as follows:

1. An intelligent fire detection system that uses OpenCV and machine learning to automatically detect fires in real-time video feeds, triggering alarms and notifications when a fire is detected, enhancing fire safety and response.
2. The system should be capable of detecting fires at their early stages, allowing for faster response and minimizing the spread of the fire.
3. The primary objective of the system is to reduce property damage and minimize the risk to human lives by providing early warning and facilitating a swift response to fires.

VII. FUTURE SCOPE

Multi-Sensor Fusion: Integrating data from multiple sensors, such as thermal cameras and smoke detectors, can enhance the reliability of fire detection systems. Fusion of visual and thermal data can improve detection accuracy, especially in scenarios with varying lighting conditions. Real-Time 3D Scene Understanding: Utilizing depth-sensing cameras like LiDAR or RGB-D cameras could enable the system to understand the 3D structure of the environment. This can aid in better distinguishing between actual fires and other sources of heat. Integration with AI-Assisted Firefighting: Fire detection systems could be integrated with AI-powered firefighting systems. For example, drones equipped with firefighting equipment could be dispatched to the detected fire location.

Conclusion

In conclusion, the development of a fire detection alarm system using OpenCV presents a significant advancement in enhancing safety and security across various environments. Leveraging the power of computer vision and image processing techniques, this system offers real-time monitoring and early detection of potential fire or smoke incidents. Real-time detection enables prompt alerts and responses, allowing occupants and emergency services to act swiftly in critical situations. The flexibility to configure sensitivity thresholds and alert mechanisms ensures adaptability to diverse environments and user requirements.

References

[1] Zahraa Shihab Al Hakeem, Hawaraa H.Abass, Haider Ismael Shahadi, “An Automatic System for Detection of Fires in Outdoor Areas” in IEEE Access. DOI: 10.1109/ICECET55527.2022.9872883 [2] “Real time Smoke and fire warning detection method based on an improved YOLOv5 model” paper by Shuyan Liu, Jianbin Fing. DOI:10.1109/PRAI55851.2022.9904105 [3] G. Sathyakala and V. Kirthika, B. Aishwarya, “Computer Vision Based Fire Detection with a Video Alert System” in IEEE Access. DOI: 10.1109/ICCSP.2018.8524216 [4] Ti Nguyen-Ti; Thuan Nguyen-Phuc; Tuan Do-Hong. “Fire detection based on video processing method” in IEEE Access. DOI: 10.1109/ATC.2013.6698087 [5] Bibek Shrestha, “Fire Detection Using Image Processing” in Metropolia University of Applied science. [6] Aman Kumar, Flavia D Gonsalves, “Computer Vision Based Fire Detection System Using OpenCV - A Case Study”, in Research & Review: Machine Learning and Cloud Computing at Matjourals.

Copyright

Copyright © 2023 Chetana Patil, Vaishnavi Andhalkar, Janhavi Dandavate, Pranjal Pokharkar, Suresh Lora. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.