Smoke Detection and Localization in Video Surveillance Applications Based on Efficient Deep CNN

Due to human causes and a dry climate, the number of forest fires reported has in-creased year after year. Many

detection strategies have been extensively investigated and put into practise in order to avoid a horrific fire disaster. Their use in

snoke detection systems will significantly enhance detection accuracy, resulting in fewer fire disasters and less ecological and

social consequences. However, because of the large memory and processing requirements for inference, the application of CNNbased smoke detection systems in real-world surveillance networks is a serious challenge. in the proposed scheme, To create a

classification model that utilizes Deep Learning to detect fires in images/video frames, allowing for early detection and saving

manual work. This model can be used to detect smoke in surveillance videos. This method can also be used to reduce the number

of accidents caused by fires in in- dustries, hospitals, and other locations. Furthermore, by taking into consideration the specific

characteristics of the situation at hand as well as the variety of smoke data, this suggested system demonstrates how a balance

between smoke detection accuracy and efficiency may be achieved