PaveSync: A Unified and Comprehensive Dataset for Pavement Distress Analysis and Classification
This provides a globally representative benchmark for pavement defect detection, enabling fair model comparisons and zero-shot transfer, though it is incremental as it builds on existing datasets by standardizing them.
The authors tackled the problem of limited generalization in automated pavement defect detection by creating a standardized dataset called PaveSync, which consolidates 52,747 images from seven countries with 135,277 bounding box annotations across 13 distress types, and demonstrated its effectiveness through benchmarking with state-of-the-art models achieving competitive performance.
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.