YOLO-Based Sequential Image Approach for Improved Smoke Detection in Early-Stage Wildfires
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105032749903
Journal Title
2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025
Rights Holder(s)
SCOPUS
Bibliographic Citation
2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025 (2025)
Suggested Citation
Thamrongweingpung S., Usanavasin S., Khowyabud N., Pipatanagovit P., Kamonsuphathawat P., Ngamsukhonratana N., Limpacharoenkul R., Galajit K., Karnjana J. YOLO-Based Sequential Image Approach for Improved Smoke Detection in Early-Stage Wildfires. 2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025 (2025). doi:10.1109/iSAI-NLP66160.2025.11320548 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115789
Title
YOLO-Based Sequential Image Approach for Improved Smoke Detection in Early-Stage Wildfires
Corresponding Author(s)
Other Contributor(s)
Abstract
Wildfires in northern Thailand, particularly during the dry season from November to May, are a major source of hazardous air pollution and sharp increases in PM2.5 levels. Despite no-burning policies, conventional monitoring methods such as lookout towers remain inadequate due to limited visibility, staffing shortages, and delayed response times. Existing smoke detection models mostly rely on single-frame analysis, which often misclassifies fog or clouds as smoke and fails to detect faint or obscured smoke plumes. This reduces the reliability of earlywarning systems in real-world deployment. The objective of this study is to evaluate whether time-series analysis can improve detection performance over single-frame baselines. We fine-tuned a YOLOv8n model on the FireSpot dataset and designed a sequential evaluation protocol that aggregates predictions across short image sequences, converting per-frame outputs into eventlevel classifications. This temporal consistency filters out transient false positives while preserving true smoke evidence. Experimental results show that per-image classification achieved a precision of 0.9780, a recall of 0.8230, an F1-score of 0.8940, and a balanced accuracy of 0.9020. The sequential framework improved performance with perfect precision of 1.0000, a recall of 0.8970, an F1-score of 0.9460, and balanced accuracy of 0.9490. These results confirm that temporal modeling significantly enhances robustness and reliability, providing a practical pathway for early-warning deployment in wildfire-prone regions.