# Automated Pavement Cracks Detection and Classification Using Deep Learning

arXiv: https://arxiv.org/abs/2406.07674 · HTML: https://mlsanity.com/paper/2406.07674/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection](https://mlsanity.com/paper/1901.06340/) (2019)
- [Vulnerable Road User Detection Using Smartphone Sensors and Recurrence Quantification Analysis](https://mlsanity.com/paper/2006.06941/) (2020)

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

- [Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing](https://mlsanity.com/paper/2409.18286/) (2024)
- [Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks](https://mlsanity.com/paper/2503.21956/) (2025)

## Abstract (arXiv)

Monitoring asset conditions is a crucial factor in building efficient transportation asset management. Because of substantial advances in image processing, traditional manual classification has been largely replaced by semi-automatic/automatic techniques. As a result, automated asset detection and classification techniques are required. This paper proposes a methodology to detect and classify roadway pavement cracks using the well-known You Only Look Once (YOLO) version five (YOLOv5) and version 8 (YOLOv8) algorithms. Experimental results indicated that the precision of pavement crack detection reaches up to 67.3% under different illumination conditions and image sizes. The findings of this study can assist highway agencies in accurately detecting and classifying asset conditions under different illumination conditions. This will reduce the cost and time that are associated with manual inspection, which can greatly reduce the cost of highway asset maintenance.