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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2501.03533 |
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| _version_ | 1866929663186567168 |
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| author | Kitsukawa, Takumi Miura, Kazuma Yumoto, Shigeki Pathak, Sarthak Moro, Alessandro Umeda, Kazunori |
| author_facet | Kitsukawa, Takumi Miura, Kazuma Yumoto, Shigeki Pathak, Sarthak Moro, Alessandro Umeda, Kazunori |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03533 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Anomaly Triplet-Net: Progress Recognition Model Using Deep Metric Learning Considering Occlusion for Manual Assembly Work Kitsukawa, Takumi Miura, Kazuma Yumoto, Shigeki Pathak, Sarthak Moro, Alessandro Umeda, Kazunori Computer Vision and Pattern Recognition 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. |
| title | Anomaly Triplet-Net: Progress Recognition Model Using Deep Metric Learning Considering Occlusion for Manual Assembly Work |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.03533 |