Pavement cracks from UAV imagery-1388

A pavement crack image dataset from UAV imagery integrated with <b>GSD</b> was established and has been made publicly available for the research community, serving as a valuable supplement to existing crack databases.A total of 1388 pavement crack images here were collected and labeled, with 304 samples identified as being of the longitudinal crack (<b>LC</b>) type, 303 samples identified as being of the transverse crack (<b>TC</b>) type, 313 samples identified as being of the obliquely oriented crack (<b>OC</b>) type, 368 samples identified as being of the alligator crack (<b>AC</b>) type, and 100 samples being identified as of the no-crack type. To ensure the deep learning-based model’s effectiveness, the datasets were divided into training, validation, and test sets in the ratio of 80%, 10%, and 10%, respectively.Please cited this reference as following if using the data.XinbaoChenX; Chang Liu; Long Chen; Xiaodong Zhu; Yaohui Zhang; Chenxi Wang ; A PavementCrack Detection and Evaluation Framework for a UAV Inspection System Based on Deep Learning,<i>Applied Sciences</i>, 2024, 14(3): 1157.<br>