mlsanityData to 14 Oct 2026

AI Driven Road Maintenance Inspection

arXiv 2106.02567 ↗First posted 4 Jun 2021cs.CV cs.AI cs.LGcited by 2

Not on the map. Why?

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.

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

9 of 16 references.

References · 9

  1. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  2. 2016Wide Residual Networkscited by 3,070
  3. 2019Object Detection in 20 Years: A Surveycited by 531
  4. 2018Deep Learning for Generic Object Detection: A Surveycited by 392
  5. 2019HarDNet: A Low Memory Traffic Networkcited by 76
  6. 2018Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphonecited by 45
  7. 2020Global Road Damage Detection: State-of-the-art Solutionscited by 16
  8. 2019RGPNet: A Real-Time General Purpose Semantic Segmentationcited by 9
  9. 2020Transfer Learning-based Road Damage Detection for Multiple Countriescited by 6

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1.

2 papers.

Similar, not cited

Close in wording, no citation either way

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  2. 2025InfraGPT Smart Infrastructure: An End-to-End VLM-Based Framework for Detecting and Managing Urban Defects
  3. 2026Edge-Optimized Vision-Language Models for Underground Infrastructure Assessment
  4. 2024REG: Refined Generalized Focal Loss for Road Asset Detection on Thai Highways Using Vision-Based Detection and Segmentation Models
  5. 2026PaveBench: A Versatile Benchmark for Pavement Distress Perception and Interactive Vision-Language Analysis
  6. 2026EG-ARSA: An Expert-Grounded Open Model for Visual Road Safety Auditing in Low-Resource Settings
  7. 2026Unleashing the Capabilities of Large Vision-Language Models for Intelligent Perception of Roadside Infrastructure
  8. 2021iCurb: Imitation Learning-based Detection of Road Curbs using Aerial Images for Autonomous Driving