Version 1
: Received: 4 September 2024 / Approved: 4 September 2024 / Online: 4 September 2024 (12:56:31 CEST)
How to cite:
Hasan, M. W.; Shanto, S.; Nayeema, J.; Rahman, R.; Helaly, T.; Rahman, Z.; Mehedi, S. T. An Explainable AI-based Modified YOLOv8 Model for Efficient Fire Detection. Preprints2024, 2024090372. https://doi.org/10.20944/preprints202409.0372.v1
Hasan, M. W.; Shanto, S.; Nayeema, J.; Rahman, R.; Helaly, T.; Rahman, Z.; Mehedi, S. T. An Explainable AI-based Modified YOLOv8 Model for Efficient Fire Detection. Preprints 2024, 2024090372. https://doi.org/10.20944/preprints202409.0372.v1
Hasan, M. W.; Shanto, S.; Nayeema, J.; Rahman, R.; Helaly, T.; Rahman, Z.; Mehedi, S. T. An Explainable AI-based Modified YOLOv8 Model for Efficient Fire Detection. Preprints2024, 2024090372. https://doi.org/10.20944/preprints202409.0372.v1
APA Style
Hasan, M. W., Shanto, S., Nayeema, J., Rahman, R., Helaly, T., Rahman, Z., & Mehedi, S. T. (2024). An Explainable AI-based Modified YOLOv8 Model for Efficient Fire Detection. Preprints. https://doi.org/10.20944/preprints202409.0372.v1
Chicago/Turabian Style
Hasan, M. W., Ziaur Rahman and Sk. Tanzir Mehedi. 2024 "An Explainable AI-based Modified YOLOv8 Model for Efficient Fire Detection" Preprints. https://doi.org/10.20944/preprints202409.0372.v1
Abstract
Fire detection is crucial for ensuring human safety and minimizing property damage. By utilizing advanced technologies, we can identify fires early, before they escalate. Autonomous fire detection systems are particularly vital in high-risk areas with minimal human presence. To address this need, we propose an automated fire-detection model based on YOLOv8. We modified the architecture of YOLOv8 and achieved impressive performances. Through comprehensive analysis of different YOLO models and their predecessors, we identified and addressed substantial gaps in this field. Also, by augmenting the framework with additional capabilities, we surpassed previous models, enabling real-time fire detection. Our proposed model achieves impressive performances, with 98% accuracy in fire detection and 97.8% in smoke detection. Moreover, recall rates for fire and smoke are 97.1% and 97.4%, respectively, with a mean average precision (mAP) accuracy of 99.1% for both cases. Finally, we applied an explainable artificial intelligence technique to interpret our proposed model and its results. This study lays the foundation for future research aimed at enhancing fire detection efficiency. The reliability of our modified model presents promising opportunities for future advancements, ensuring increased effectiveness and precision in efficient fire detection.
Keywords
fire detection; modified YOLOv8; deep learning; computer vision; explainable artificial intelligence; EigenCAM
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
