Comparative Assessment of YOLO Segmentation Extensions for Intelligent Fire Detection

With the growing frequency of fire incidents, the demand for rapid and accurate fire detection technologies has become increasingly critical. In this study, we evaluate segmentation-based object detection models YOLO (You Only Look Once) v5-seg, YOLOv8-seg, and YOLOv11-seg for their ability to detect flames and smoke under identical experimental conditions. A total of 5,000 fire images were collected and split into training, validation, and test datasets. The same hardware environment and hyperparameter settings were used for model training to ensure a fair comparison. The experimental results reveal that YOLOv11-seg achieved the best overall performance, with a Precision of 0.710, Recall of 0.570, F1-score of 0.632, and mAP (mean Average Precision) 50 of 0.600. Notably, YOLOv11-seg achieved the highest Recall and mAP values for smoke detection, underscoring its effectiveness in identifying smoke—a critical factor for early fire detection. In terms of efficiency, YOLOv8-seg demonstrated the fastest inference speed, while YOLOv5-seg offered advantages in lightweight model size. However, YOLOv11-seg provided a balanced trade-off between computational cost and detection accuracy, making it the most suitable model for real-world fire response scenarios. Accordingly, this study proposes YOLOv11-seg as a robust baseline model for segmentation-based fire detection and provides a foundational reference for future research on deep learning-driven intelligent fire video analysis.