Liu Zhen, Wu Wenxiu, Gu Xingyu, Cui Bingyan
Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing, 211189, China.
Highway, Port and Transportation Management Center, Jinhua, 321000, China.
Data Brief. 2024 Nov 5;57:111111. doi: 10.1016/j.dib.2024.111111. eCollection 2024 Dec.
The PaveDistress dataset contains high-resolution images of road surface distresses, including cracks, repairs, potholes, and background images without defects. The data were collected using a specialized pavement inspection vehicle along the S315 highway in China. The vehicle was equipped with a Basler raL2048-80km line scan camera and infrared laser-assisted lighting, capturing images at 1mm intervals with a resolution of 3854 × 2065 pixels. The images were taken every 2 meters across various lighting conditions, including daylight, dusk, and in challenging environments such as tunnels and cloudy weather. The dataset is organized into distinct categories, covering transverse cracks, longitudinal cracks, map cracks, and more, enabling detailed categorization of pavement distresses. Each image represents a real-world road coverage area of 3.9m × 2.1m, allowing for accurate measurements of defect dimensions. This dataset supports the development of deep learning models for non-destructive detection of road defects, providing valuable resources for civil engineering research and practical applications in road maintenance systems. The dataset can be reused for tasks such as image classification, object detection, and segmentation, enabling researchers to create advanced machine learning models for road distress detection and assessment. By providing high-quality, diverse images, the PaveDistress dataset offers significant potential for research in automated pavement condition monitoring and management systems.
PaveDistress数据集包含道路表面病害的高分辨率图像，包括裂缝、修补处、坑洼以及无缺陷的背景图像。这些数据是使用专门的路面检测车辆在中国的S315高速公路上采集的。该车辆配备了一台Basler raL2048 - 80km线扫描相机和红外激光辅助照明设备，以1毫米的间隔拍摄图像，分辨率为3854×2065像素。这些图像是在每隔2米的距离拍摄的，涵盖了各种光照条件，包括白天、黄昏以及在隧道和多云天气等具有挑战性的环境中。该数据集被组织成不同的类别，涵盖横向裂缝、纵向裂缝、网状裂缝等，能够对路面病害进行详细分类。每张图像代表一个3.9米×2.1米的实际道路覆盖区域，便于精确测量缺陷尺寸。这个数据集支持用于道路缺陷无损检测的深度学习模型的开发，为土木工程研究和道路维护系统的实际应用提供了宝贵的资源。该数据集可用于图像分类、目标检测和分割等任务，使研究人员能够创建用于道路病害检测和评估的先进机器学习模型。通过提供高质量、多样化的图像，PaveDistress数据集在自动路面状况监测和管理系统的研究中具有巨大的潜力。
