CRACK500 with noisy annotation masks

Published: 21 November 2024| Version 1 | DOI: 10.17632/wddt4gbttd.1
Contributors:
, Jize Zhang

Description

This is the repository to host the dataset to train noisy labelled crack segmentation algorithm proposed by Zhang, et al. (upcoming) in the article "SelectSeg: Uncertainty-based selective training and prediction for accurate crack segmentation under limited data and noisy annotations". In the zip folder, you can find "train_crop_mask_(10,20,50,100)pct", corresponding to the case where 10%, 20%, 50%, and 100% masks have been replaced by their noisy version. For other files, including training images, and validation/test dataset, we refer the interested readers to https://github.com/fyangneil/pavement-crack-detection for the original development.

Files

Institutions

Hong Kong University of Science and Technology

Categories

Computer Vision, Structural Engineering, Image Segmentation, Crack

Funding

Innovation and Technology Fund

PRP/011/23FX

Research Grants Council, University Grants Committee

C6044-23GF

Hong Kong University of Science and Technology

BGF.2003.009

Licence