# Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distresses

arXiv: https://arxiv.org/abs/1910.11123 · HTML: https://mlsanity.com/paper/1910.11123/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [PaveCap: The First Multimodal Framework for Comprehensive Pavement Condition Assessment with Dense Captioning and PCI Estimation](https://mlsanity.com/paper/2408.04110/) (2024)
- [Advancing Pavement Distress Detection in Developing Countries: A Novel Deep Learning Approach with Locally-Collected Datasets](https://mlsanity.com/paper/2408.05649/) (2024)
- [The 1st Data Science for Pavements Challenge](https://mlsanity.com/paper/2206.04874/) (2022)
- [Deep Domain Adaptation for Pavement Crack Detection](https://mlsanity.com/paper/2111.10101/) (2021)
- [RDD4D: 4D Attention-Guided Road Damage Detection And Classification](https://mlsanity.com/paper/2501.02822/) (2025)
- [A Computer Vision-assisted Approach to Automated Real-Time Road Infrastructure Management](https://mlsanity.com/paper/2202.13285/) (2022)
- [A Computer Vision Enabled damage detection model with improved YOLOv5 based on Transformer Prediction Head](https://mlsanity.com/paper/2303.04275/) (2023)
- [AI and Remote Sensing for Resilient and Sustainable Built Environments: A Review of Current Methods, Open Data and Future Directions](https://mlsanity.com/paper/2507.01547/) (2025)
- [A Benchmark Dataset for Spatially Aligned Road Damage Assessment in Small Uncrewed Aerial Systems Disaster Imagery](https://mlsanity.com/paper/2512.12128/) (2025)
- [PaveBench: A Versatile Benchmark for Pavement Distress Perception and Interactive Vision-Language Analysis](https://mlsanity.com/paper/2604.02804/) (2026)

## Abstract (arXiv)

Automated pavement distresses detection using road images remains a challenging topic in the computer vision research community. Recent developments in deep learning has led to considerable research activity directed towards improving the efficacy of automated pavement distress identification and rating. Deep learning models require a large ground truth data set, which is often not readily available in the case of pavements. In this study, a labeled dataset approach is introduced as a first step towards a more robust, easy-to-deploy pavement condition assessment system. The technique is termed herein as the Pavement Image Dataset (PID) method. The dataset consists of images captured from two camera views of an identical pavement segment, i.e., a wide-view and a top-down view. The wide-view images were used to classify the distresses and to train the deep learning frameworks, while the top-down view images allowed calculation of distress density, which will be used in future studies aimed at automated pavement rating. For the wide view group dataset, 7,237 images were manually annotated and distresses classified into nine categories. Images were extracted using the Google Application Programming Interface (API), selecting street-view images using a python-based code developed for this project. The new dataset was evaluated using two mainstream deep learning frameworks: You Only Look Once (YOLO v2) and Faster Region Convolution Neural Network (Faster R-CNN). Accuracy scores using the F1 index were found to be 0.84 for YOLOv2 and 0.65 for the Faster R-CNN model runs; both quite acceptable considering the convenience of utilizing Google maps images.