AI-Driven Road Maintenance Inspection v2: Reducing Data Dependency & Quantifying Road Damage

TL;DR

This paper introduces an AI-based pipeline for road damage detection that reduces data annotation costs and assesses repair urgency, improving generalization and safety in road maintenance.

Contribution

It presents a novel automated labeling approach using few-shot learning and out-of-distribution detection, along with a risk assessment for prioritizing repairs.

Findings

Models trained with the pipeline generalize better to unseen data.

The approach reduces the need for extensive manual annotation.

It provides estimates of maintenance urgency for safer roads.

Abstract

Road infrastructure maintenance inspection is typically a labor-intensive and critical task to ensure the safety of all road users. Existing state-of-the-art techniques in Artificial Intelligence (AI) for object detection and segmentation help automate a huge chunk of this task given adequate annotated data. However, annotating videos from scratch is cost-prohibitive. For instance, it can take an annotator several days to annotate a 5-minute video recorded at 30 FPS. Hence, we propose an automated labelling pipeline by leveraging techniques like few-shot learning and out-of-distribution detection to generate labels for road damage detection. In addition, our pipeline includes a risk factor assessment for each damage by instance quantification to prioritize locations for repairs which can lead to optimal deployment of road maintenance machinery. We show that the AI models trained with these techniques can not only generalize better to unseen real-world data with reduced requirement for human annotation but also provide an estimate of maintenance urgency, thereby leading to safer roads.