基于改进YOLOv5的林火检测技术研究

Reserch on forest fire detection base on improved YOLOv5

为提高林火检测的准确率和检测速度,增强林火检测模型的实用性,提出了一种改进YOLOv5的林火检测算法。该算法将改进后的MobileViT作为YOLOv5的骨干网络,使网络能够更有效地提取林火特征信息,同时为进一步降低模型复杂度,采用深度可分离卷积替代模型中的普通卷积,在训练阶段引入了Mosaic数据增强的方法,以提高模型的泛化性。结果表明:改进后模型的林火检测精确率提高了2.25%,mAP提高了4.48%,检测速度提高了4帧/s,检测准确率和检测速度均取得了良好的效果。改进后模型能够很好地检测林火,提高了林火检测模型的实用性。

In order to improve the accuracy and speed of forest fire detection and enhance the practicability of forest fire detection model,an improved YOLOv5 forest fire detection algorithm was proposed.In this algorithm,the improved MobileViT was used as the backbone network of YOLOv5,so that the network could extract forest fire feature information more effectively.Meanwhile,in order to further reduce the complexity of the model,depthwise separable convolution was used to replace the common convolution in the model,and Mosaic data enhancement method was introduced in the training stage to improve the generalization of the model.The results show that the forest fire detection accuracy of the improved model is increased by 2.25%,mAP by 4.48%,and detection speed by 4 frames/s.Both the detection accuracy and detection speed have achieved good results.The improved model can detect forest fire well and improve the practicability of forest fire detection model.The algorithm in this paper is more competent for the task of forest fire detection.