A Coarse-to-Fine Intelligent Inspection Framework for Building Fire Hazard Recognition

Building fire safety inspection is a knowledge-intensive engineering task that requires reliable hazard recognition under complex visual conditions, limited labeled data, and strict regulatory accountability. To address these challenges, this paper proposes a coarse-to-fine intelligent inspection framework for building fire hazard recognition and regulation-grounded reporting. The framework first performs binary hazard screening and then refines positive or uncertain cases into specific hazard categories, thereby aligning the inference process with practical inspection workflows. A self-supervised DINOv2 Vision Transformer is adopted as the visual backbone, and a small-sample adaptation strategy is developed by combining staged fine-tuning, a lightweight SE-based classification head, and task-aligned knowledge distillation. In addition, an Agentic RAG compliance layer is introduced to retrieve, verify, and present clause-level regulatory evidence while suppressing hallucinated or unverifiable citations. Experiments on a real-world building fire hazard image dataset show that the proposed framework achieves stable recognition performance, outperforms representative CNN-, supervised Transformer-, and self-supervised Transformer-based baselines, and improves the faithfulness of regulation-grounded reporting. The results suggest that the proposed framework provides a feasible prototype-level pathway toward intelligent and auditable fire safety inspection, while broader multi-site validation and robustness evaluation remain necessary for future deployment.