Intelligent Spatial Estimation for Fire Hazards in Engineering Sites: An Enhanced YOLOv8-Powered Proximity Analysis Framework
This study proposes an enhanced dual-model YOLOv8 framework for intelligent fire detection and proximity-aware risk assessment, extending conventional vision-based monitoring beyond simple detection to actionable hazard prioritization. The system is trained on a dataset of 9,860 annotated images to segment fire and smoke across complex environments. The framework combines a primary YOLOv8 instance segmentation model for fire and smoke detection with a secondary object detection model pretrained on the COCO dataset to identify surrounding entities such as people, vehicles, and infrastructure. By integrating the outputs of both models, the system computes pixel-based distances between detec...
| Authors | Ammar K. AlMhdawi, Nonso Nnamoko, Alaa Mashan Ubaid |
|---|---|
| Primary category | cs.CV |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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