  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.
