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

arXiv: https://arxiv.org/abs/2210.03570 · HTML: https://mlsanity.com/paper/2210.03570/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 13 references that are in the map.
- First posted on arXiv 7 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [3D Packing for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/1905.02693/) (2019)
- [CrossTransformers: spatially-aware few-shot transfer](https://mlsanity.com/paper/2007.11498/) (2020)
- [Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone](https://mlsanity.com/paper/1801.09454/) (2018)
- [Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsics](https://mlsanity.com/paper/2202.03131/) (2022)
- [Global Road Damage Detection: State-of-the-art Solutions](https://mlsanity.com/paper/2011.08740/) (2020)
- [Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation](https://mlsanity.com/paper/2103.02451/) (2021)
- [RGPNet: A Real-Time General Purpose Semantic Segmentation](https://mlsanity.com/paper/1912.01394/) (2019)
- [Calibrating Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2009.07714/) (2020)
- [AI Driven Road Maintenance Inspection](https://mlsanity.com/paper/2106.02567/) (2021)

## Built on by (most cited first, 1 of 1)

- [Depth3DLane: Fusing Monocular 3D Lane Detection with Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2507.13857/) (2025)

## Abstract (arXiv)

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.