# AI Driven Road Maintenance Inspection

arXiv: https://arxiv.org/abs/2106.02567 · HTML: https://mlsanity.com/paper/2106.02567/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [HarDNet: A Low Memory Traffic Network](https://mlsanity.com/paper/1909.00948/) (2019)
- [Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone](https://mlsanity.com/paper/1801.09454/) (2018)
- [Global Road Damage Detection: State-of-the-art Solutions](https://mlsanity.com/paper/2011.08740/) (2020)
- [RGPNet: A Real-Time General Purpose Semantic Segmentation](https://mlsanity.com/paper/1912.01394/) (2019)
- [Transfer Learning-based Road Damage Detection for Multiple Countries](https://mlsanity.com/paper/2008.13101/) (2020)

## Built on by (most cited first, 2 of 2)

- [AI-Driven Road Maintenance Inspection v2: Reducing Data Dependency & Quantifying Road Damage](https://mlsanity.com/paper/2210.03570/) (2022)
- [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 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.