  Road infrastructure maintenance inspection is typically a labour-intensive
and critical task to ensure the safety of all the road users. In this work, we
propose a detailed methodology to use state-of-the-art techniques in artificial
intelligence and computer vision to automate a sizeable portion of the
maintenance inspection subtasks and reduce the labour costs. The proposed
methodology uses state-of-the-art computer vision techniques such as object
detection and semantic segmentation to automate inspections on primary road
structures such as the road surface, markings, barriers (guardrails) and
traffic signs. The models are mostly trained on commercially viable datasets
and augmented with proprietary data. We demonstrate that our AI models can not
only automate and scale maintenance inspections on primary road structures but
also result in higher recall compared to traditional manual inspections.
