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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.09069 |
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| _version_ | 1866915849449766912 |
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| author | AlMhdawi, Ammar K. Nnamoko, Nonso Ubaid, Alaa Mashan |
| author_facet | AlMhdawi, Ammar K. Nnamoko, Nonso Ubaid, Alaa Mashan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_09069 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Intelligent Spatial Estimation for Fire Hazards in Engineering Sites: An Enhanced YOLOv8-Powered Proximity Analysis Framework AlMhdawi, Ammar K. Nnamoko, Nonso Ubaid, Alaa Mashan Computer Vision and Pattern Recognition 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. |
| title | Intelligent Spatial Estimation for Fire Hazards in Engineering Sites: An Enhanced YOLOv8-Powered Proximity Analysis Framework |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.09069 |