This paper proposes a few-shot learning approach that can successfully learn and detect new construction objects when only a few training data are given. The proposed approach includes few-shot model design and meta-learning processes. To validate the approach, the authors conducted experiments using a popular construction benchmark dataset, AIMDataset. Even if only 20 training images were provided to a new construction object, the few-shot learning could build an object detection model with the mean Average Precision of 73.1% on average, whereas the performance of the existing supervised learning was limited to 36.5%. The results imply that the proposed approach can successfully learn and detect new types of construction objects only with few labeled images given, enabling to reduce the number of training images while maximizing the model performance. It would be then possible to save human efforts required for data labeling and enhance the practicality of vision-based construction monitoring systems.

本文提出了一种仅需少量训练数据就可以成功学习和检测新建筑对象的快速学习方法。所提出的方法包括少量的模型设计和元学习过程。为了验证该方法，作者使用了流行的建筑基准数据集AIMDataset进行了实验。即使仅将20个训练图像提供给一个新的构造对象，通过少量学习也可以建立平均平均精度为73.1％的对象检测模型，而现有的监督学习的性能限制为36.5％。结果表明，所提出的方法仅需给出少量标记图像即可成功学习和检测新型建筑对象，能够减少训练图像的数量，同时最大化模型性能。这样就可以节省数据标记所需的人力，并提高基于视觉的施工监控系统的实用性。