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. Labeling 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. • Introduce a public dataset with segmentation labels for reinforced concrete defects. • Statistical detection of labeling inconsistencies and analysis of performance impact. • YOLOv8L-seg, DeepLabV3, and U-Net baselines established, YOLOv8L-seg performs best. • False negatives are main failure mode of the model, likely due to lack of data. • Embedded implementation of model shows good performance using FP16 precision.