CRACK500 with noisy annotation masks
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you can find "train_crop_mask_(10,20,50,100)pct", corresponding to the case where 10%, 20%, 50%, and 100% masks have been replaced by their noisy version.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
Categories
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