Vision-Based Early Fire and Smoke Detection for Smart Factory Applications Using FFS-YOLO

Early-stage fire and smoke detection through visual analysis is crucial for industrial safety and hazard prevention. However, detecting fire and smoke in factories using surveillance cameras poses challenges due to the small size of target objects. To address these challenges, we introduce a refined single-stage detector called FFS-YOLO (Factory Fire Smoke - YOLO). Our approach incorporates the Parameter-Free Attention Module (SimAM) and ResNet-SimMix module into the Backbone and Head of YOLOv7 to enhance key feature extraction. Additionally, we modify the model architecture by adding an extra prediction head to facilitate the fusion of features at multiple scales, specifically for small-scale object detection. Experimental results conducted on our fire and smoke dataset demonstrate the effectiveness of the FFS-YOLO model, achieving an average mAP, Precision, and Recall of 0.92, 0.91, and 0.90, respectively. The performance of the proposed model outperforms existing relevant competitors in the field. The findings of this research contribute significantly to the advancement of early fire detection and prevention in factory settings.