Generative AI-driven edge-cloud system for intelligent road infrastructure inspection
Naveed Ejaz, A B M Bodrul Alam, Salimur Choudhury
Abstract
The rapid advancement of edge computing and artificial intelligence (AI) has transformed infrastructure inspection by enabling real-time monitoring of roads, bridges, and pipelines. However, high bandwidth consumption, latency, and limited interpretability remain key challenges. This paper presents a novel hybrid edge-cloud framework for intelligent road infrastructure inspection, combining lightweight AI on edge devices with generative AI in the cloud. The Edge AI Module, built on MobileNetV3, performs real-time anomaly detection and generates concise reports with GPS-tagged severity information. Anomalous data is selectively transmitted to the cloud, where advanced models—EfficientNet-B4, MiDaS DPT-Large, and T5-XL—refine classification, estimate depth, compute road quality metrics, and generate structured, actionable reports. The system is evaluated on two diverse datasets: RDD2022, a multinational road damage dataset, and UAV-PDD2023, a high-resolution aerial imagery dataset. Results demonstrate the framework's real-time capability, achieving an edge inference time of 30 to 50 ms and reducing bandwidth usage by 50 to 70%. Cloud processing provides fine-grained analysis and high accuracy in natural language reporting. This dual-tier architecture balances low-latency anomaly detection and in-depth analysis, providing a scalable and interpretable solution for large-scale infrastructure monitoring in dynamic environments.