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.
Forest fire preventionDeep learningTarget detectionYOLOv5 algorithmAttention mechanism
维普
万方数据
