Machine Learning based Autonomous Fire Combat Turret

The time lag between the identification and the initiation of the actuation protocol is more in conventional fire combat system. This in turn increases the response time resulting in financial loss as well as injuries to human beings. In this paper an efficient method of fire combat is proposed to eliminate resource loss. This system extinguishes fire before it reaches its destructive level. It eliminates all the flaws of the conventional fire extinguishers and improves the damage limitation by raising an alarm. Further by applying HAAR cascade classifier machine learning algorithm, accuracy of 70-75 % is achieved to detect fire. It also provides minimum latency and optimal response in detecting fires and differentiating them from false triggers. It is observed that the response time of proposed fire combat system is 2-4 seconds. The automatic mode is reliable in the presence of multiple units that are deployed in the same area of interest. The system is able to cover the entire hemispheric 3D volume of the room as per the industrial and domestic safety standards.