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.

中文翻译：

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钢筋混凝土结构的分割数据集


本文提供了一个包含 14,805 张 RGB 图像的数据集，其中包含分段标签，用于自主机器人检测钢筋混凝土缺陷。建立了 YOLOv8L-seg、DeepLabV3 和 U-Net 分割模型的基线。对标签不一致进行统计处理，并分析它们对模型性能的影响。采用错误识别工具来检查模型的误差模式。该论文表明，YOLOv8L-seg 表现最佳，验证 mIOU 分数高达 0.59。发现标签不一致对模型性能的影响可以忽略不计，而包含更多数据提高了性能。假阴性被确定为主要故障模式。结果突出了数据可用性对基于深度学习的模型性能的重要性。缺乏公开可用的数据被认为是导致假阴性的重要因素。为了解决这个问题，本文倡导在建筑社区中增加开源方法。