Intelligent Spatial Estimation for Fire Hazards in Engineering Sites: An Enhanced YOLOv8-Powered Proximity Analysis Framework
It addresses fire safety monitoring for industrial and resource-constrained settings, though it is incremental as it builds on existing YOLOv8 and COCO models.
This study tackles fire hazard assessment in engineering sites by developing an enhanced YOLOv8 framework that detects fire/smoke and nearby objects to compute proximity-based risk scores, achieving over 90% precision, recall, F1, and mAP@0.5 above 91%.
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 detected fire regions and nearby objects and converts these values into approximate real-world measurements using a pixel-to-meter scaling approach. This proximity information is incorporated into a risk assessment mechanism that combines fire evidence, object vulnerability, and distance-based exposure to produce a quantitative risk score and alert level. The proposed framework achieves strong performance, with precision, recall, and F1 scores exceeding 90% and mAP@0.5 above 91%. The system generates annotated visual outputs showing fire locations, detected objects, estimated distances, and contextual risk information to support situational awareness. Implemented using open-source tools within the Google Colab environment, the framework is lightweight and suitable for deployment in industrial and resource-constrained settings.