Small target smoke detection algorithm based on improved YOLOv5
In order to solve the problem of low accuracy of smoke detection for small target in fire,a smoke detection algorithm based on improved YOLOv5 was proposed.Firstly,FFA module was introduced into the backbone network,so that the model focused on the extraction of smoke feature information of small target.Secondly,the convolutional layer module was replaced by MPDH module to improve the detection part of the prediction head layer in the YOLOv5 algorithm,which was used to improve the positioning accuracy of small target smoke.Finally,the experimental results and analysis were carried out on the proprietary data set.The results show that the improved YOLOv5 small target smoke detection algorithm achieves 85.4％target detection accuracy,and the accuracy and recall rate are improved by 3.2％and 6.3％respectively compared with the original algorithm.
small target smokesmoke detection algorithmYOLOv5feature fusion attention(FFA)multi-scale pyramid decoupling head(MPDH)
万方数据
维普
