Efficient attention-based networks for fire and smoke detection

Abstract

To address limitations in current flame and smoke detection models, including difficulties in handling irregularities, occlusions, large model sizes, and real-time performance issues, this work introduces FS-YOLO, a lightweight attention-based model. FS-YOLO adopts an efficient architecture for feature extraction capable of capturing long-range information, overcoming issues of redundant data and inadequate global feature extraction. The model incorporates squeeze-enhanced-axial-C2f to enhance global information capture without significantly increasing computational demands. Additionally, the improved VoVNet-GSConv-cross stage partial network refines semantic information from higher-level features, reducing missed detections and maintaining a lightweight model. Compared to YOLOv8n, FS-YOLO achieves a 1.4% increase and a 1.0% increase in mAP0.5 and mAP0.5:0.95, respectively, along with a 1.3% improvement in precision and a 1.0% boost in recall. These enhancements make FS-YOLO a promising solution for flame and smoke detection, balancing accuracy and efficiency effectively.