Segmentation Dataset for Reinforced Concrete Construction

TL;DR

This paper introduces a large dataset of reinforced concrete images with segmentation labels for robotic inspection, evaluates multiple models, and emphasizes the importance of data availability and open-source sharing for improved defect detection.

Contribution

It provides a comprehensive dataset, benchmarks segmentation models, and analyzes label inconsistencies and error modes to advance automated concrete defect inspection.

Findings

YOLOv8L-seg achieves highest validation mIOU of 0.59

Label inconsistencies have minimal impact on model performance

More data improves segmentation accuracy and reduces false negatives

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.