Multi-Classifir Fire and Smoke Detectior Using Deep Learning
1Deshmukh Pratiksha, 2 Dhage Manasi, 3 Dhiwar Priti, 4Wadate Neha, 5Prof. Pallavi Kohakade
1,2,3,4Students, Shri Chhatrapati Shivaji Maharaj College of Engineering,
5Ass.Prof, Shri Chhatrapati Shivaji Maharaj College of Engineering,
1Department Of Computer Engineering,
1Shri Chhatrapati Shivaji Maharaj College of Engineering, Ahmednagar, India
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Abstract : - Fire is one of the most destructive powers that has been a double-edged sword. Although it is highly beneficial and provides energy when it is handled in an effective manner, it can be quite lethal if it is allowed to continue unchecked. Combustion, as well as the conversion and release of energy, is what makes fire so destructive. It is a violent process that has the potential to unleash enormous amounts of harm. This is a very unfavorable condition that has the potential to result in a truly catastrophic event. A great number of species of flora and fauna have become extinct as a result of the recent years, which have been marked by a big number of disastrous wildfires that have resulted in a large scale loss of life and property. This is one of the most deadly occurrences that has occurred in recent years. The most significant issue is that there is not yet a fire detection method that is both efficient and practical. Consequently, the purpose of this study piece is to propose an efficient multi-classifier strategy for fire detection. This approach identifies the color of the fire, the form of the fire, and the movement of the fire, in addition to the detection of smoke through the use of the convolution neural network. According to the extensive experimental results that demonstrate the superiority of the suggested multiclassifier fire and smoke detection strategy, this approach has shown to be one of the most effective approaches for fire detection. This is obvious through the fact that it has been one of the most effective techniques
Key Words: — Convolution neural network, Multi-classifier, Fire shape, Fire Motion , Fore color, Temporal effect
