• Dr. Sreenivasa B. C.
• M Shreya
• Rishita Goyal
• Sudhiksha R.
• Tanya P. Bobichan

A forest fire is an unplanned fire that breaks out in a wilderness setting like a forest or prairie. Forest fires have proven to be a threat to humans and wildlife creatures. Early detection of forest fires will decrease the severity preventing huge loss of ecosystems and its effect on global conditions. The forest fire detection model that is developed can be set up to analyze and process images from security cameras, drones, and satellites. Dataset consisting of various images of forests and surroundings resembling forests is used and the images are classified into two categories: “ fire ” and “ smoke ”. To identify the existence or onset of a forest fire in an image efficiently, a deep-learning model is created and trained. In this study, we propose a forest fire detection system that makes use of YOLOv5 for detection and classification and DenseNet for feature extraction. Further the nearest authorities will soon be informed after the specifics of the incident are known.

Machine learning, deep learning, convolutional neural network, forest fire detection, object detection, YOLO, DenseNet

"FOREST FIRE DETECTION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.m573-m579, April-2023, Available :http://www.jetir.org/papers/JETIR2304C75.pdf

"FOREST FIRE DETECTION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppm573-m579, April-2023, Available at : http://www.jetir.org/papers/JETIR2304C75.pdf