Generative AI-driven edge-cloud system for intelligent road infrastructure inspection
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025019152 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2590-1230 |