Multi-scale fire target detection algorithm using YOLO-fire

Accurate and real-time fire detection is crucial for industrial production and daily life. However, the variable form of fire and the significant differences in visual characteristics across its different stages pose great challenges to precise fire prevention and control. To address this issue, a multi-scale fire target detection algorithm using YOLO-fire was proposed by improving the YOLOv8 model. This model introduced new layer structures and attention mechanism, replaced new feature fusion modules and loss functions. By introducing a small-target detection P2 layer, the model’s ability to detect early-stage fires is improved. The coordinate attention mechanism is integrated into the layer structures of multi-scale target detection, enhancing the capture of target location information and channel relationships, thereby focusing more on the target regions. The Neck network structure was optimized by adopting a BiFPN_F strategy for different feature layers, which strengthened the cross-scale representation of fire features and controlled the parameter count of the designed model. The WIoU loss function was employed to optimize the regression process, improving fire source localization accuracy in complex scenarios, enhancing model robustness, and increasing detection precision. Experimental results on fire datasets demonstrated that YOLO-fire could effectively detect multi-scale fire targets in various scenarios. Compared to the baseline model (YOLOv8n), YOLO-fire achieves improvements of 1.37% in accuracy, 1.25% in mAP50-95, and 0.35% in <italic>F</italic>1-score, while reducing parameters by 3.79%. Furthermore, compared to current mainstream target detection algorithms, YOLO-Fire achieved optimal detection performance while reducing network parameters and computational complexity. This research provided effective technical support for fire safety prevention and control in related fields.