Safeguard: Intelligent Fire Extinguisher

Abstract

Firefighting is a hazardous and time-sensitive operation. Conventional fire suppression systems rely heavily on manual intervention, which can lead to delays and increased risks. To address this, we propose Safeguard: Intelligent Fire Extinguisher, an autonomous fire detection and suppression system using computer vision and an Arduino-based control mechanism. The system detects fire in real-time using image processing techniques, determines its precise location, and actuates a servo motor-controlled water pump to extinguish the flames. This ensures faster response times, minimal water wastage, and reduced human involvement, making it suitable for industrial and hazardous environments.

Introduction

Summary:

Fire hazards cause serious risks and economic losses, with traditional fire detection systems often responding too late and manual firefighting posing safety risks. Advances in computer vision, embedded systems, and robotics enable development of autonomous fire suppression systems.

The Safeguard system uses camera-based fire recognition combined with flame and temperature sensors to detect fires early. Controlled by an Arduino microcontroller, it employs servo motors to precisely direct a water pump nozzle for targeted fire suppression, minimizing water wastage. The system continuously monitors and adjusts its actions via feedback loops to ensure effective extinguishing.

Methodology:

• Fire detection is done using real-time video processing (HSV color space segmentation) plus sensor validation to reduce false positives.

• The Arduino processes fire location data via Cartesian mapping to control nozzle direction.

• Fire suppression uses servo-controlled water pumps activated only upon confirmed fire detection.

• The system calibrates dynamically to environmental conditions.

Results:

• Fast detection (~0.5 seconds after ignition) and precise targeting (95% accuracy).

• Effective suppression of small to medium fires within 5 seconds.

• Low false positive rate (<2%).

• Performs well under low light and moderate smoke, though dense smoke limits camera visibility.

Advantages:

• Reduces human risk by automating fire response.

• Efficient water usage by targeting only the fire area.

• Scalable for industrial and commercial applications.

• Faster and more precise than traditional heat-based sprinklers.

Limitations:

• Camera-based detection struggles in dense smoke.

• Fixed nozzle range limits coverage area.

• Power dependency requires reliable energy sources.

• Outdoor conditions like wind may affect suppression accuracy.

Future Improvements:

• Incorporate infrared or thermal imaging for better smoke penetration.

• Add mobility and obstacle avoidance for wider coverage.

• Integrate AI for improved fire classification and false positive reduction.

• Enable IoT-based remote monitoring and control.

Conclusion

The Safeguard: Intelligent Fire Extinguisher successfully detects, targets, and extinguishes fires autonomously, significantly reducing response times and increasing safety. The integration of computer vision, sensor-based validation, and automated control mechanisms ensures that fire suppression is carried out with precision and efficiency, reducing risks to human lives and property damage. The study highlights the potential of AI-powered fire suppression in industrial and commercial settings, where rapid response and accuracy are crucial. The system’s ability to operate without human intervention makes it particularly suitable for high-risk environments, including chemical plants, warehouses, and remote locations where manual firefighting may not be feasible. Despite its advantages, the system has certain limitations. Smoke density and environmental factors may affect detection accuracy, requiring further refinements in thermal imaging and AI-based classification for better performance in challenging conditions. Additionally, power dependency and operational range constraints highlight the need for integrating backup power solutions and mobile navigation enhancements. Future work will focus on improving adaptability and scalability. Enhancing AI-driven fire recognition, integrating IoT-based remote monitoring, and incorporating robotic mobility will significantly boost the system’s capabilities. These improvements will allow the system to function in diverse environments, extending its application to smart buildings, factories, and autonomous fire safety networks. Further research should explore the possibility of multiagent coordination, where multiple units work collaboratively to contain larger fires effectively. Advanced machine learning models could also be incorporated to predict fire spread patterns and optimize suppression strategies. In conclusion, the Safeguard: Intelligent Fire Extinguisher represents a significant step forward in modern firefighting technology. By refining its detection algorithms, expanding mobility functions, and integrating AI-driven automation, the system has the potential to become an essential component of next generation fire safety solutions.

References

Monica P. Suresh, et al., \"An Arduino Uno Controlled Fire Fighting Robot,\" I-SMAC, 2022. [2] Pengcheng Liu, et al., \"Robot-assisted Smart Firefighting,\" ICAC, 2016. [3] AbeerImdoukh, et al., \"Semi-autonomous Indoor Firefighting UAV,\" ICAR, 2017. [4] Recent advancements in deep learning for fire detection, IEEE, 2021. [5] Computer vision-based early fire detection in construction sites, Journal of Civil Engineering and Management, 2022. [6] Thermal imaging flame detection models for firefighting robots, MDPI, 2023.

Copyright

Copyright © 2025 Sayed Mustafa Mahdi Rizvi, Namir Shah, Mahek Tiwari, Samrat Mishra. 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.