DETECTION OF FIRE COMBUSTION IN FORESTS USING DEEP LEARNING

DETECTION OF FIRE COMBUSTION IN FORESTS USING DEEP LEARNING

Jellela.Vinila1, Kareddy. Sai Sathwik Reddy2,,Marepalli.Nikitha3,

Mr. B. Kiran Kumar 4,

1,2,3,4B.Tech. Student, Department of Computer Science and Engineering,

Vinivinila9@gmail.com, saisathwik07092002@gmail.com, nikithadaas141@gmail.com,  kirankumarb.cse@nmrec.edu.in

5Assistant Professor, Department of Computer Science and Engineering,

Nalla Malla Reddy Engineering College, Hyderabad, India

Abstract— Forest fires are a major environmental issue, creating economic and ecological damage while endangering human lives. There are typically about 100,000 wildfires in the United States every year. Over 9 million acres of land have been In this paper, the author uses CNN-convolutional neural networks to detect fire with the help of live video footage through anti-fire surveillance systems. The paper proposes YOLOv2 convolutional neural network is  the best solution for detecting fire and smoke both indoor and outdoor environment. You only look once (YOLO) is a deep learning model for object detection, YOLOv2 is the next version which has been upgraded to rectify the setbacks of YOLO namely the inaccuracy to locate and mark the region of interest in the images and the lower recall rate compared to other region-oriented algorithms. Thus, increasing the efficiency of the architecture. They started with an input image of size 128x128x3.

Top of FormKeywords— Fire detection, Forest fires, Deep learning, Artificial neural networks,