Abstract: This study presents an indoor vision-based fire detection system that integrates a YOLOv8n object detection model with a Reinforcement Learning-based Optimization Algorithm (ROA) for hyperparameter tuning. The research investigates three key aspects: 1) the effectiveness of ROA in improving model performance, 2) the optimal smart camera resolution and placement for indoor fire detection, and 3) the implementation of a real-time dual-channel user notification system. The BantaySunog model iteratively adjusted a few hyperparameters using the Reinforcement Learning-based Optimization Algorithm (ROA) [Talaat & Gamel, 2023]. An episodic framework was used for training, with 15 episodes of 20 epochs each, for a maximum of 300 epochs. Each episode's top weights were carried over to the following one. To balance exploration and exploitation, ROA employed an epsilon-greedy policy with an epsilon value that decreased from 0.9 to 0.2. Experimental results show that while ROA reduced training time and yielded a more conservative prediction strategy, it did not consistently outperform the baseline YOLOv8n in terms of detection metrics such as recall and mAP50-95. Camera deployment tests identified that positioning cameras away from direct light sources significantly improved detection success, with both elevation and resolution contributing to overall system performance. Finally, a dual-channel alert mechanism combining Firebase Cloud Messaging (FCM) and Telerivet SMS API enabled the timely delivery of fire alerts, aligning with real-world standards. The findings contribute to the development of reliable and accessible fire detection systems, especially for densely populated residential areas with limited infrastructure.

Patrick D. Cerna, Harvey C. Quijada, Michael Joseph E. Ortaliz and Kenneth Charles H. Saluna. “Hybrid Reinforcement Learning-Based Hyper-Parameter Optimization with Yolov8 Indoor Fire Recognition”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.01611104

@article{Cerna2025,
title = {Hybrid Reinforcement Learning-Based Hyper-Parameter Optimization with Yolov8 Indoor Fire Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01611104},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01611104},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {11},
author = {Patrick D. Cerna and Harvey C. Quijada and Michael Joseph E. Ortaliz and Kenneth Charles H. Saluna}
}