Segmentation Dataset for Reinforced Concrete Construction
Abstract
This paper provides a dataset of 14,805 RGB images with segmentation labels for autonomous robotic inspection of reinforced concrete defects. Baselines for the YOLOv8L-seg, DeepLabV3, and U-Net segmentation models are established. Labelling inconsistencies are addressed statistically, and their influence on model performance is analyzed. An error identification tool is employed to examine the error modes of the models. The paper demonstrates that YOLOv8L-seg performs best, achieving a validation mIOU score of up to 0.59. Label inconsistencies were found to have a negligible effect on model performance, while the inclusion of more data improved the performance. False negatives were identified as the primary failure mode. The results highlight the importance of data availability for the performance of deep learning-based models. The lack of publicly available data is identified as a significant contributor to false negatives. To address this, the paper advocates for an increased open-source approach within the construction community.
Keywords
Cite
@article{arxiv.2407.09372,
title = {Segmentation Dataset for Reinforced Concrete Construction},
author = {Patrick Schmidt and Lazaros Nalpantidis},
journal= {arXiv preprint arXiv:2407.09372},
year = {2025}
}
Comments
The ConRebSeg Dataset can be found under the following DOI: https://doi.org/10.11583/DTU.26213762 Corresponding code to download additional data and initialize the dataset under https://github.com/DTU-PAS/ConRebSeg This work is an accepted manuscript up for publication in the Elsevier journal "Automation in Construction"