In recent years, the advancement of AI technologies has accelerated the development of smart factories. In particular, the automatic monitoring of product assembly progress is crucial for improving operational efficiency, minimizing the cost of discarded parts, and maximizing factory productivity. However, in cases where assembly tasks are performed manually over multiple days, implementing smart factory systems remains a challenge. Previous work has proposed Anomaly Triplet-Net, which estimates assembly progress by applying deep metric learning to the visual features of products. Nevertheless, when visual changes between consecutive tasks are subtle, misclassification often occurs. To address this issue, this paper proposes a robust system for estimating assembly progress, even in cases of occlusion or minimal visual change, using a small-scale dataset. Our method leverages a Quadruplet Loss-based learning approach for anomaly images and introduces a custom data loader that strategically selects training samples to enhance estimation accuracy. We evaluated our approach using a image datasets: captured during desktop PC assembly. The proposed Anomaly Quadruplet-Net outperformed existing methods on the dataset. Specifically, it improved the estimation accuracy by 1.3% and reduced misclassification between adjacent tasks by 1.9% in the desktop PC dataset and demonstrating the effectiveness of the proposed method.

翻译：近年来，人工智能技术的进步加速了智能工厂的发展。其中，产品装配进度的自动监控对于提升运营效率、最小化废弃零件成本以及最大化工厂生产力至关重要。然而，在装配任务需人工操作且跨越多日完成的情况下，智能工厂系统的实施仍面临挑战。先前的研究提出了异常三元组网络（Anomaly Triplet-Net），该方法通过对产品的视觉特征应用深度度量学习来估计装配进度。然而，当连续任务之间的视觉变化细微时，经常发生误分类。为解决此问题，本文提出了一种鲁棒的装配进度估计系统，即使在存在遮挡或视觉变化极小的情况下，也能利用小规模数据集进行有效估计。我们的方法利用基于四元组损失（Quadruplet Loss）的学习策略处理异常图像，并引入了一种自定义数据加载器，该加载器通过策略性地选择训练样本来提升估计精度。我们使用在台式电脑装配过程中采集的图像数据集对所提方法进行了评估。在数据集上，所提出的异常四元组网络（Anomaly Quadruplet-Net）的性能优于现有方法。具体而言，在台式电脑数据集上，其估计精度提升了1.3%，相邻任务间的误分类率降低了1.9%，从而验证了所提方法的有效性。