基于YOLOv5的早期林火监测方法研究

Research on Early Forest Fire Detection Method Based on YOLOv5

研究采用深度学习技术,基于YOLOv5算法识别森林火灾初期图像,提高预警效率,减少生态及社会损失。在YOLOv5算法的基础上进行了网络结构优化,提出了一种能够在野外识别林火中火焰特征的模型方法,通过对数据集采用数据预处理、数据增强、模型训练与优化等步骤得到检测模型,通过指标评估得到模型的检测精度。该算法在林火监测中具有较高的准确率和实时性,能够有效降低火灾发生的概率,减少火灾带来的损失。基于YOLOv5改进网络的早期林火监测算法研究具有重要的理论价值和广泛的应用前景。

This study aims to use deep learning technology to identify early forest fire images based on YOLOv5 algorithm,improve early warning efficiency,and reduce ecological and social losses.Based on the YOLOv5 algorithm,network structure optimization is carried out,and a model method is proposed that can recognize flame characteristics in forest fires in the wild.Through data preprocessing,data augmentation,model training and optimization of the dataset,a detection model is obtained.Finally,the detection accuracy of the model is evaluated through index evaluation.This algorithm has high accuracy and real-time performance in forest fire monitoring,and can effectively reduce the probability of fire occurrence and the losses caused by fire.The research on the improved YOLOv5 network-based early forest fire monitoring algorithm has important theoretical value and wide application prospects.