Fire detection and ranging method based on binocular vision and improved YOLOv8n
Aiming at the problems of missing detection and misdetection,large number of model parameters and difficult location in fire detection,a lightweight fire detection and ran-ging method based on binocular vision and improved YOLOv8n was proposed.Pictures were taken by binocular camera,and the improved detection algorithm YOLOv8n-AEM and the existing ranging algorithm SGBM were used for detection and ranging.Firstly,variable ker-nel convolution AKConv and EMA attention mechanisms are introduced into the backbone network to effectively extract fire features by constructing irregular convolutional nuclei.Then,the C2f-SCConv module is constructed in the neck network to reduce the model param-eters and improve the detection speed through feature recombination.Secondly,the loss func-tion is improved based on the minimum point distance to solve the problem of missing detec-tion and false detection caused by overlapping fire source and light source.Finally,the detec-tion head of small target is added to improve the detection ability of small flame.The experi-mental results show that the improved detection algorithms P,R and mAP are 83.6％,76.4％and 83.6％respectively,which are improved by 2.5％,3.6％and 4.8％respectively.The parameter number and model size were 2.54 M and 5.1 MB,which decreased by 15.3％and 15％,respectively.The accuracy error of ranging is less than 2.5％,which proves that the improved method can accurately complete the fire detection and ranging.
fire detectionbinocular visiondistance measurementYOLOv8nlight weight
万方数据
维普
