The construction industry has been traditionally slow in adopting digital technologies. However, these are becoming increasingly necessary due to a plentitude of challenges, such as a shortage of skilled labor and decreasing productivity levels compared to other industries. Autonomous robotic systems can alleviate this problem, but the software development process for these systems is heavily driven by data, a resource usually challenging to find in the construction domain due to the lack of public availability. In our work, we therefore provide a dataset of 14,805 RGB images with segmentation labels for reinforced concrete construction and make it publicly available. We conduct a detailed analysis of our dataset and discuss how to deal with labeling inconsistencies. Furthermore, we establish baselines for the YOLOv8L-seg, DeepLabV3, and U-Net segmentation models and investigate the influence of data availability and label inconsistencies on the performance of these models. Our study showed that the models are precise in their predictions but would benefit from more data to increase the number of recalled instances. Label inconsistencies had a negligible effect on model performance, and we, therefore, advocate for a crowd-sourced dataset to boost the development of autonomous robotic systems in the construction industry.

翻译：建筑行业在采用数字技术方面历来进展缓慢。然而，由于面临诸多挑战，如熟练劳动力短缺、与其他行业相比生产率水平下降等，这些技术正变得越来越必要。自主机器人系统可以缓解这一问题，但这些系统的软件开发过程高度依赖数据，而由于缺乏公开可用的资源，数据在建筑领域通常难以获取。因此，在我们的工作中，我们提供了一个包含14,805张RGB图像及分割标签的钢筋混凝土施工数据集，并将其公开。我们对数据集进行了详细分析，并讨论了如何处理标注不一致的问题。此外，我们为YOLOv8L-seg、DeepLabV3和U-Net分割模型建立了基线，并研究了数据可用性和标注不一致对这些模型性能的影响。我们的研究表明，这些模型的预测精度较高，但需要更多数据来提高召回实例的数量。标注不一致对模型性能的影响可以忽略不计，因此我们主张通过众包数据集来推动建筑行业自主机器人系统的发展。