Performance Evaluation of Fire and Smoke Detection with Object based DNN Algorithm

Abstract

The consequence of forest fire is so great that we need to prevent it from happening with all of our resources. However, the time and human resources required to cover all the forest is very high. In this paper, we use object based Deep Learning detection algorithm, YOLOV5, to address the issue of detecting forest fire and smoke. We collected 13,924 images of forest fire and smoke and manually labeled them. We used YOLOV5n to learn the features. We solved overfitting problem via mosaic data augmentation, non-transfer learning, and hyperparameter evolution. YOLOV5n shows that mAP 0.5 is about 14.1% higher than the official YOLOV5n model. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).