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Fire-YOLO: Balancing Accuracy and Efficiency for Edge-Based Early Fire Warning Systems | ||
| Al-Noor Journal for Information Technology and Cybersecurity | ||
| Article 8, Volume 2, Issue 2, December 2025, Pages 56-61 PDF (634.56 K) | ||
| Document Type: Research paper | ||
| DOI: 10.69513/jncs.v2.i2.a8 | ||
| Authors | ||
| Ahmed yassin Mohammad* 1; Abdulamir Abdullah Karim2 | ||
| 1Informatics Institute for Postgraduate Studies, Iraqi Commission for Computers & Informatics, Baghdad 10071, Iraq College of Political Science, University of Mosul, Mosul 41002, Iraq | ||
| 2College of Computer Science, University of Technology, Baghdad, Iraq | ||
| Abstract | ||
| Balancing detection fidelity for amorphous hazards like fire and smoke against edge-device constraints remains a critical challenge. Prevailing methods compound architectural complexity or enforce rigid geometric losses—yet such approaches falter when confronting fire’s stochastic morphology. Introducing Fire-YOLO, a streamlined detector built by embedding Channel Attention Modules (C2f-SE) into YOLOv8n’s backbone, the hypothesis that detection fidelity stems not from structural depth, but from directed attention—a principle embedded in Fire-YOLO's architecture. These modules act as dynamic semantic filters, amplifying flame chromatic signatures and smoke textures while muting environmental clutter. Rigorous ablation exposes pitfalls of alternatives—inception blocks and MPDIoU losses degrade localization accuracy by failing to generalize across fire’s non-stationary spatial dynamics. Fire-YOLO avoids these traps. It achieves 79.5% mean Average Precision (mAP), computed as the average over IoU thresholds from 0.5 to 0.95 with 1.6% increments, 78% recall, and sustained 141 FPS inference on NVIDIA Tesla T4. There is no compromise between rigor and speed. This architecture redefines feasibility for low-cost, real-time fire warning systems. | ||
| Keywords | ||
| Attention Mechanism; Squeeze and Excitation; Deep Learning; Wildfire Detection | ||
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