Abstract: In order to solve the problems of early warning delay and reduced recognition accuracy of traditional fire detection models caused by insufficient feature extraction and excessive model complexity when dealing with complex scenes,a target detection model based on improved YOLOv10,which can be deployed on terminal devices,is proposed to achieve rapid and accurate detection of both smoke and fire. It is named YOLO-BFEPS(YOLO bi-directional fusion with enhanced partial self-attention) new fire detection model. Firstly,the PSA module is improved to enhance spatial semantic feature extraction,solve the problems of information loss and increased computational complexity caused by channel dimensionality reduction modeling cross-channel relationships,improve detection accuracy,and record the improved module as E-PSA(enhanced partial self-attention). Secondly,scale fusion is carried out based on BiFPN’s idea of bidirectional cross-connection of feature layers,and the neck structure of YOLOv10 is redesigned,and the fusion of information from low feature layers is innovatively increased,which greatly reduce the model parameters and computational complexity while maintaining accuracy. The bottleneck structure of C2f module is replaced by a faster block structure,the lightweight design of the model is implemented and it is called C2f-Faster. Finally,experiments are carried out to verify the effectiveness of the proposed model on multiple datasets. The results show that the proposed model can improve the Precision and mAP@0.5 by 5.9% and 1.4% respectively on the basis of reducing the number of parameters by 35.5% and the computational complexity by 17.6%

Key words: Efficient attention, Multi-scale feature, Bi-directional weighted feature pyramid networks, Fire detection, YOLOv10, Lightweight, Computer vision, Deep learning