Hybrid Reinforcement Learning-Based Hyper-Parameter Optimization with Yolov8 Indoor Fire Recognition

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.

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