URDD: An open dataset for urban roadway disease detection and classificationMendeley Data
Shuaiqi Liu,
Wenjing Jiang,
Yue Yu,
Lei Ren,
Chunbo Li,
Qi Hu
Affiliations
Shuaiqi Liu
College of Electronic and Information Engineering, Hebei University, Baoding 071002, China; Machine Vision Technology Innovation Center of Hebei Province, Baoding 071002, China; Corresponding authors at: College of Electronic and Information Engineering, Hebei University, Baoding 071002, China.
Wenjing Jiang
College of Electronic and Information Engineering, Hebei University, Baoding 071002, China; Machine Vision Technology Innovation Center of Hebei Province, Baoding 071002, China
Yue Yu
College of Electronic and Information Engineering, Hebei University, Baoding 071002, China; Machine Vision Technology Innovation Center of Hebei Province, Baoding 071002, China
Lei Ren
519 Team of North China Geological Exploration Bureau, China
Chunbo Li
519 Team of North China Geological Exploration Bureau, China
Qi Hu
College of Electronic and Information Engineering, Hebei University, Baoding 071002, China; Machine Vision Technology Innovation Center of Hebei Province, Baoding 071002, China; Corresponding authors at: College of Electronic and Information Engineering, Hebei University, Baoding 071002, China.
Urban traffic accidents have become more common in recent years due to the rising number of motorized vehicles, climate change, and outdated subsurface drainage systems. Traditional road disease detection methods involve collecting road data using ground-penetrating radar and manually analyzing the data. This process is time-consuming and subjective. Deep learning, especially convolutional neural networks (CNNs), has proven highly effective in image recognition and object detection. By applying these techniques to road disease detection, both the efficiency and accuracy of detection can be significantly improved. To support this, we have created a specialized road disease dataset designed for object detection and classification tasks. The release of this dataset aims to promote the use of artificial intelligence (AI) in autonomous road disease detection and classification, enhancing detection efficiency and contributing to better urban road maintenance and management.
