CrackAirport: A Dataset for Segmentation of Cracks in Airport Pavements

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Authors: Zefeng Lyu, Andrew Junfang Yu, Christopher Starr
Cited by: 0

Abstract

CrackAirport contains 2,226 images of 512x512 pixels with annotations. These images include typical environmental patterns of airport pavements such as aircraft, T-hangars, vegetation, airport markings and signs, and marks of previous maintenance. The images were captured using a Sony ILCE-7RM4A camera mounted on a drone flying at an altitude of 100 feet. The original images were collected from various local airports in Tennessee. These images were annotated and then cropped to a size of 512x512 pixels. To balance the ratio of crack pixels to background pixels, some images with very few crack pixels were excluded. Additionally, some images with special noise were added to help a neural network, such as U-Net, recognize noise.

Fields of Study & Subjects

Infrastructure Maintenance and Monitoring Advanced Neural Network Applications Industrial Vision Systems and Defect Detection

Publication details

Journal / source
Mendeley Data
Publication date
16 February 2026
Language
EN
Document type
Dataset