PaveSync: A Unified and Comprehensive Dataset for Pavement Distress Analysis and Classification

TL;DR

PaveSync introduces a large, standardized dataset of over 52,000 images from seven countries, covering 13 pavement distress types, to improve defect detection and model generalization across diverse real-world conditions.

Contribution

This paper presents the first comprehensive, standardized benchmark dataset for pavement distress analysis, enabling consistent training, evaluation, and fair comparison of detection models globally.

Findings

State-of-the-art models achieved competitive performance on the dataset.

The dataset captures diverse real-world conditions, enhancing model robustness.

Standardization facilitates zero-shot transfer to new environments.

Abstract

Automated pavement defect detection often struggles to generalize across diverse real-world conditions due to the lack of standardized datasets. Existing datasets differ in annotation styles, distress type definitions, and formats, limiting their integration for unified training. To address this gap, we introduce a comprehensive benchmark dataset that consolidates multiple publicly available sources into a standardized collection of 52747 images from seven countries, with 135277 bounding box annotations covering 13 distinct distress types. The dataset captures broad real-world variation in image quality, resolution, viewing angles, and weather conditions, offering a unique resource for consistent training and evaluation. Its effectiveness was demonstrated through benchmarking with state-of-the-art object detection models including YOLOv8-YOLOv12, Faster R-CNN, and DETR, which achieved competitive performance across diverse scenarios. By standardizing class definitions and annotation formats, this dataset provides the first globally representative benchmark for pavement defect detection and enables fair comparison of models, including zero-shot transfer to new environments.