Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Edge AI Enabled Road Inspection Vehicle for On Field Road Damage Assessment and Traffic Data Acquisition

Author(s) Dr. Yogesh S. Khandekar
Country India
Abstract Real-time road monitoring systems play a vital role in enhancing transportation safety, traffic efficiency, and infrastructure maintenance by continuously assessing road surface conditions and traffic dynamics; however, conventional stationary deployments significantly restrict spatial coverage and limit scalability across large road networks. As a continuation of our previous work on an edge-AI- based intelligent road monitoring system using YOLOv8, this paper presents a hardware-driven, field-deployable road inspection vehicle designed for continuous on-road assessment. The proposed system integrates a Raspberry Pi–based edge computing unit with a vision module, GPS receiver, and a motorized inspection platform to enable real- time detection of potholes, cracks, and vehicles during active field traversal. The YOLOv8 model is executed locally on the embedded platform to ensure low-latency inference, while road damage severity is estimated using a hybrid metric that combines detection confidence scores and bounding-box area measurements. Each detected event is geotagged and time-stamped, enabling spatial mapping and historical analysis, and an event-driven communication strategy is employed to selectively transmit critical data to a cloud dashboard, thereby minimizing bandwidth usage. Field experiments conducted on urban and semi-urban roads demonstrate reliable detection accuracy, stable real- time performance, and effective road coverage under varying traffic density and lighting conditions, validating the feasibility of extending edge-AI road monitoring systems into real-world, field operational deployments for intelligent transportation infrastructure.
Keywords Edge Artificial Intelligence, Road Inspection System, YOLOv8, Embedded Vision, Field Deployment, Intelligent Transportation Systems, Road Damage Detection, Traffic Monitoring
Field Engineering
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-04-09
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E-ISSN 3048-7641
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