Deep Learning Wildfire Detection to Increase Fire Safety with Yolov8

Authors

  • Pandu Wicaksono, Rezki Yunanda, Panji Arisaputra, Zahra Nabila Izdihar

Keywords:

: computer vision, deep learning, forest fire, wildfire detection, YOLO

Abstract

Object detection involves using computer vision algorithms to identify and locate objects in an image or video. In the context of wildfire detection, object detection can be used to identify features such as flames, smoke, and heat sources in satellite or drone imagery and to alert authorities to the presence of a wildfire. YOLOv8 (You Only Look Once) is a popular object detection algorithm widely used for various applications, including wildfire detection. The YOLOv8 object detection algorithm could be used to help reduce the impact of wildfires on communities and the environment. After extensive preprocessing and a well-structured 25-epoch experimental phase, the model performed well with a mean Average Precision (mAP) of 0.6, precision of 0.7, and recall of 0.57. This study advances wildfire detection methods and provides significant information.

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Published

24.03.2024

How to Cite

Pandu Wicaksono. (2024). Deep Learning Wildfire Detection to Increase Fire Safety with Yolov8. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 4383 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6190

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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