Edge AI-Enabled Road Fixture Monitoring System

• Published in: Buildings (Basel) Vol. 14; no. 5; p. 1220
• Main Authors: Younesi Heravi, Moein, Dola, Israt Sharmin, Jang, Youjin, Jeong, Inbae
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2024
• Subjects: Automation; Crowdsourcing; Data collection; Decision making; Deep learning; digital twin; Digital twins; edge computing; Efficiency; Emergency preparedness; Emergency response; Environmental conditions; Fixtures; Forecasts and trends; Geospatial data; Global positioning systems; GPS; Infrastructure; Inspections; Internet of Things; Lasers; localization; Monitoring systems; Object recognition; Remote monitoring; Remote sensing; Response time; road fixture; Road maintenance; Roads; Roads & highways; Safety and security measures; Semantics; Sensors; Smart cities; Smartphones; Traffic control; Traffic flow; Traffic signs; Transportation planning; Vehicles
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings14051220

Effective monitoring of road fixtures is essential for urban safety and functionality. However, traditional inspections are time-consuming, costly, and error prone, while current automated solutions struggle with high initial setup costs, limited flexibility preventing wide adaptation, and reliance on centralized processing that can delay response times. This study introduces an edge AI-based remote road fixture monitoring system which automatically and continuously updates the information of the road digital twin (DT). The main component is a small-sized edge device consisting of a camera, GPS, and IMU sensors designed to be installed in typical cars. The device captures images, detects the fixture, and estimates their location by employing deep learning and feature matching. This information is transmitted to a dedicated cloud server and represented on a user-friendly user interface. Experiments were conducted to test the system’s performance. The results showed that the device could successfully detect the fixture and estimate their global coordinates. Outputs were marked and shown on the road DT, proving the integrated and smooth operation of the whole system. The proposed Edge AI device demonstrated that it could significantly reduce the data size by 80–84% compared to traditional methods. With a satisfactory object detection accuracy of 65%, the system effectively identifies traffic poles, stop signs, and streetlights, integrating these findings into a digital twin for real-time monitoring. The proposed system improves road monitoring by cutting down on maintenance and emergency response times, increasing the ease of data use, and offering a foundation for an overview of urban road fixtures’ current state. However, the system’s reliance on the quality of data collected under varying environmental conditions suggests potential improvements for consistent performance across diverse scenarios.