Segmentation Dataset for Reinforced Concrete Construction
This addresses the problem of limited data for deep learning-based defect inspection in reinforced concrete construction, which is an incremental contribution by establishing a new dataset and baselines.
The paper tackles the lack of publicly available data for autonomous robotic inspection of reinforced concrete defects by providing a dataset of 14,805 RGB images with segmentation labels, and it demonstrates that YOLOv8L-seg performs best with a validation mIOU score of up to 0.59.
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.