# Deep Machine Learning Approach to Develop a New Asphalt Pavement Condition Index

arXiv: https://arxiv.org/abs/2004.13314 · HTML: https://mlsanity.com/paper/2004.13314/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [PaveCap: The First Multimodal Framework for Comprehensive Pavement Condition Assessment with Dense Captioning and PCI Estimation](https://mlsanity.com/paper/2408.04110/) (2024)
- [PaveSAM Segment Anything for Pavement Distress](https://mlsanity.com/paper/2409.07295/) (2024)
- [The 1st Data Science for Pavements Challenge](https://mlsanity.com/paper/2206.04874/) (2022)
- [An Improved ResNet50 Model for Predicting Pavement Condition Index (PCI) Directly from Pavement Images](https://mlsanity.com/paper/2504.18490/) (2025)
- [Enhancing Pothole Detection and Characterization: Integrated Segmentation and Depth Estimation in Road Anomaly Systems](https://mlsanity.com/paper/2504.13648/) (2025)
- [ST-ResGAT: Explainable Spatio-Temporal Graph Neural Network for Road Condition Prediction and Priority-Driven Maintenance](https://mlsanity.com/paper/2603.14107/) (2026)

## Abstract (arXiv)

Automated pavement distress detection via road images is still a challenging issue among pavement researchers and computer-vision community. In recent years, advancement in deep learning has enabled researchers to develop robust tools for analyzing pavement images at unprecedented accuracies. Nevertheless, deep learning models necessitate a big ground truth dataset, which is often not readily accessible for pavement field. In this study, we reviewed our previous study, which a labeled pavement dataset was presented as the first step towards a more robust, easy-to-deploy pavement condition assessment system. In total, 7237 google street-view images were extracted, manually annotated for classification (nine categories of distress classes). Afterward, YOLO (you look only once) deep learning framework was implemented to train the model using the labeled dataset. In the current study, a U-net based model is developed to quantify the severity of the distresses, and finally, a hybrid model is developed by integrating the YOLO and U-net model to classify the distresses and quantify their severity simultaneously. Various pavement condition indices are developed by implementing various machine learning algorithms using the YOLO deep learning framework for distress classification and U-net for segmentation and distress densification. The output of the distress classification and segmentation models are used to develop a comprehensive pavement condition tool which rates each pavement image according to the type and severity of distress extracted.