Automating Fire Detection and Suppression with Computer Vision: A Multi-Layered Filtering Approach to Enhanced Fire Safety and Rapid Response

A computer vision-based integrated fire detection and automated suppression device capable of real-time functioning is proposed to enhance the fire safety. The developed multilayered algorithm considers color based clue detection and thereafter incorporates three filtration stages ‘Centroid Analysis’, ‘Histogram Analysis’ and ‘Variance Analysis’ for successful fire detection. Results from the proposed algorithm has been compared and validated against standard video datasets and was found to have an overall accuracy of 95.26% with 91.61% true positive detection rate, only 8.39% of false detection in positive fire videos and true negative rate of 98.91% with only 1.09% of false detection in negative nonfire videos. Additionally, our algorithm showed an average improvement of 7.95% in accuracy and 9.43% in precision over existing algorithms, demonstrating its sensitivity and reliability for effective fire detection and suppression. The algorithm also includes unique fire localization techniques to locate the detected fire, which was integrated with an Arduino based suppression unit to provide a real- time autonomous fire suppression. Laboratory-scale experimental validation has shown practical significance of the proposed system for any kind of personal, industrial, indoor, or outdoor environmental applications with a high precision value of 99.51% and a recall value of 95.93%.