Autoadaptive Flame Detection and Classification Using Deep Learning of FastFlameNet CNN

Abstract

Image processing technologies in the domain of pattern recognition have many successful researches and implementations. In that sequence, earlier detection of fire from the video footage of the surveillance cameras is an interesting and promising technique that serves mankind and nature as well. The traditional and existing methods of fire detection in the video frames are advantageous in industry-based applications. But whereas these techniques are applied to detect forest fire in a wider area, they have their limitations of inadequate output due to interferences caused by the sunlight and other natural attributes. To improve the detection efficiency using optical flow algorithms and to estimate the direction of the flame, a novel flame detection technique from the video frames using Optimal flow algorithm and the estimation of the fire flow direction using the Deep learning CNN FastFlameNet algorithm is explained in detail in this article. The performance of the proposed architecture is measured using the performance indices like Accuracy, precision, recall, F-Measure. It was estimated that about 97% of the performance accuracy was obtained from the proposed framework.