# Forest fire prediction using image processing

## TL;DR

This paper introduces an improved model for predicting forest fires using image processing, achieving high accuracy for better early warning systems.

## Contribution

The novel YOLOv5-PSG model enhances forest fire detection accuracy and confidence levels for real-time prediction.

## Key findings

- The YOLOv5-PSG model achieves an average recognition accuracy rate of 93.1% after 300 training rounds.
- The model reaches an accuracy rate of approximately 0.802 and a confidence level of about 0.965.

## Abstract

Forest fires pose a significant threat to public safety and the environment, and harmful pollutants spread rapidly in areas covered by vegetation. Early detection is very important for preventing forest fires from evolving into catastrophic fires. The traditional prediction methods have relatively low accuracy. They can only identify fires clearly after they occur, making it difficult to meet the requirements of precise real-time detection. The YOLOv5-PSG model proposed in this paper improves the YOLOv5 model. After 300 rounds of training, the average recognition accuracy rate of mAP can reach 93.1%, and the accuracy rate can reach approximately 0.802. After 300 rounds of training and learning, the confidence level can reach about 0.965. This improvement makes fire early warning and prediction more comprehensive and effective, ultimately protecting human life and the environment by mitigating the impact of wildfires.