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arXiv · 2501.03533

Anomaly Triplet-Net: Progress Recognition Model Using Deep Metric Learning Considering Occlusion for Manual Assembly Work

Takumi Kitsukawa·Kazuma Miura·Shigeki Yumoto·Sarthak Pathak·Alessandro Moro·Kazunori Umeda
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Abstract

In this paper, a progress recognition method consider occlusion using deep metric learning is proposed to visualize the product assembly process in a factory. First, the target assembly product is detected from images acquired from a fixed-point camera installed in the factory using a deep learning-based object detection method. Next, the detection area is cropped from the image. Finally, by using a classification method based on deep metric learning on the cropped image, the progress of the product assembly work is estimated as a rough progress step. As a specific progress estimation model, we propose an Anomaly Triplet-Net that adds anomaly samples to Triplet Loss for progress estimation considering occlusion. In experiments, an 82.9% success rate is achieved for the progress estimation method using Anomaly Triplet-Net. We also experimented with the practicality of the sequence of detection, cropping, and progression estimation, and confirmed the effectiveness of the overall system.

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Takumi Kitsukawa, Kazuma Miura, Shigeki Yumoto, Sarthak Pathak, Alessandro Moro, Kazunori Umeda. 2025-01-07. Anomaly Triplet-Net: Progress Recognition Model Using Deep Metric Learning Considering Occlusion for Manual Assembly Work. https://doi.org/10.1080/01691864.2024.2422968

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