Enhanced Real-Time Fire Detection Systems Using Deep Learning and Differentiating Between Dangerous and Non-Dangerous Fires

Fire is a major hazard in many disasters, creating risks to public safety and the surrounding environment. This study aims to improve the accuracy and reliability of fire detection using deep learning, addressing the key limitations of traditional sensor systems, such as latency and poor adaptability. The proposed model presents a customized Fire-Smoke-YOLOv8x Model trained from scratch on a dataset of 100,000 images representing diverse fire and smoke conditions paired with adaptive algorithms to differentiate dangerous from non-dangerous fire scenarios and achieved a performance with 98.2 % precision, 97.3% recall, and 94% mAP@50. The model processes RGB video under varied lighting and environments and achieves strong detection results across a wide range of fire and smoke scenarios. Incorporating thermal or multispectral data, as noted in future work, could further improve performance under extremely low visibility conditions. This framework supports real-time surveillance in smart cities, transport hubs, and industrial safety, where fast and accurate detection is critical. It combines fine-grained detection with risk-aware post-processing, providing a high-performance and scalable solution for real-world fire detection.

@article{Youssef2026,
  title     = {Enhanced Real-Time Fire Detection Systems Using Deep Learning and Differentiating Between Dangerous and Non-Dangerous Fires},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Mohamed Youssef and Mohamed Marie and Sarah Naiem},
  doi       = {10.14569/IJACSA.2026.0170632},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170632}
}