From Pixels to Semantics: Edge AI for UAV-Based Critical Infrastructure Inspection

2026-09-16

AI Summary

论文探讨了使用轻量级视觉语言模型在边缘设备上实现无人机基础设施检查，从物体检测转向语义理解，以解决传统手动检查成本高和危险的问题。

Abstract

Critical infrastructure assets such as bridges, tunnels, dams, and power line networks require timely and scalable inspection. While conventional manual inspection remains costly and hazardous, unmanned aerial vehicle (UAV)-based inspection has emerged as an efficient alternative for monitoring difficult-to-access structures. Existing UAV inspection pipelines have evolved from cloud-centric offline processing toward edge-based perception using lightweight object detectors such as YOLO for real- time defect localization. This article explores the transition toward fully edge-native semantic inspection powered by lightweight vision language models (VLMs), where UAVs move beyond object detection toward contextual structural understanding. It categorizes existing UAV inspection architectures, identifies their key system challenges and architectural requirements, and experimentally assesses the feasibility of semantic edge intelligence on NVIDIA Jetson UAV-class hardware using the COCO-Bridge dataset. The evaluation integrates a fine-tuned YOLO-26M for object localization and a lightweight SmolVLM-256 for semantic reasoning. Finally, it outlines future directions toward agentic, autonomous, trustworthy, and collaborative semantic UAV inspection across the edge-cloud continuum.

Problem

• critical infrastructure inspection
• unmanned aerial vehicle (UAV)
• edge-based perception
• semantic understanding
• vision language models (VLMs)

Innovation

• Edge AI
• Semantic Inspection
• Lightweight VLMs
• UAV