Offline UAV Inspection with Audited LLM-Assisted Analytics: A Deployable Framework Integrating Computer Vision and Natural Language Querying

Field-based infrastructure inspection often occurs under intermittent or absence connectivity, limiting the applicability of cloud-dependent analytical systems. Existing approaches primarily focus on detection accuracy or edge processing, but rarely address the integration of reliable analytics, data traceability, and deployment constraints within a unified framework. To address this gap, we present OffInspect-LLM, an offline inspection platform integrating controlled LLM-assisted analytical interaction. The system combines an inspection pipeline for object detection with a constrained natural language interface that generates validated SQL queries, ensuring safe and traceable data interaction. Experimental evaluation on a dataset of 1600 images demonstrated stable multi-seed held-out test performance, achieving a deployment-oriented mean held-out test mAP@50:95 of 0.617 across five random seeds, while the highest exploratory single-run validation result reached 0.714 under fixed initialization conditions. The primary deployment-oriented evaluation corresponds to the multi-seed held-out test performance rather than the peak single-run validation result. In addition to predictive performance, the system achieves per-image inference times below 3 s on GPU, reliable batch processing, and scalable geospatial visualization exceeding 10,000 detections. The primary contribution lies in the integration of detection, structured data management, and audited querying within an offline-first architecture, enabling traceable and deployment-oriented inspection workflows through constrained analytical interaction under realistic operational conditions.