AI Driven Road Maintenance Inspection

TL;DR

This paper presents an AI-based methodology utilizing computer vision techniques like object detection and semantic segmentation to automate road maintenance inspections, reducing labor costs and increasing inspection recall.

Contribution

It introduces a comprehensive AI-driven approach for automating primary road structure inspections using state-of-the-art computer vision models trained on diverse datasets.

Findings

Automated inspections achieve higher recall than manual methods.

Models effectively detect road surface, markings, barriers, and signs.

Method reduces labor costs and scales inspection processes.

Abstract

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.