Comparative Assessment of YOLO Segmentation Extensions for Intelligent Fire Detection

• 발행기관: 한국화재소방학회
• 저자명: Sugi Choi Heejun Kwon Jiwon Choi Sangwon Lee Haiyoung Jung
• 간행물 정보: 『International Journal of Fire Science and Engineering』Vol. 39, No. 3, 26~36쪽, 전체 11쪽
• 주제분류: 공학 > 공학일반
• 파일형태: PDF
• 발행일자: 2025.09.30

국문 초록

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.

목차

1. Introduction

2. Real-time object detection model

3. Experiment

4. Experiment Results

5. Conclusions

Author Contributions

Conflicts of Interest

Acknowledgments

References

키워드

Fire detection Smoke detection Image segmentation Object detection YOLO