Latent Space Search-Based Adaptive Template Generation for Enhanced Object Detection in Bin-Picking Applications
Template matching is a common approach in bin-picking tasks. However, it often struggles in complex environments, such as those with different object poses, various background appearances, and varying lighting conditions, due to the limited feature representation of a single template. Additionally,...
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| Format: | Article |
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MDPI AG
2024-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/18/6050 |
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| author | Songtao Liu Yaonan Zhu Tadayoshi Aoyama Masayuki Nakaya Yasuhisa Hasegawa |
| author_facet | Songtao Liu Yaonan Zhu Tadayoshi Aoyama Masayuki Nakaya Yasuhisa Hasegawa |
| author_sort | Songtao Liu |
| collection | DOAJ |
| description | Template matching is a common approach in bin-picking tasks. However, it often struggles in complex environments, such as those with different object poses, various background appearances, and varying lighting conditions, due to the limited feature representation of a single template. Additionally, during the bin-picking process, the template needs to be frequently updated to maintain detection performance, and finding an adaptive template from a vast dataset poses another challenge. To address these challenges, we propose a novel template searching method in a latent space trained by a Variational Auto-Encoder (VAE), which generates an adaptive template dynamically based on the current environment. The proposed method was evaluated experimentally under various conditions, and in all scenarios, it successfully completed the tasks, demonstrating its effectiveness and robustness for bin-picking applications. Furthermore, we integrated our proposed method with YOLO, and the experimental results indicate that our method effectively improves YOLO’s detection performance. |
| format | Article |
| id | doaj-art-ef3eeddcb5214bff8ecd449163b71d35 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ef3eeddcb5214bff8ecd449163b71d352025-08-20T01:55:51ZengMDPI AGSensors1424-82202024-09-012418605010.3390/s24186050Latent Space Search-Based Adaptive Template Generation for Enhanced Object Detection in Bin-Picking ApplicationsSongtao Liu0Yaonan Zhu1Tadayoshi Aoyama2Masayuki Nakaya3Yasuhisa Hasegawa4Department of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya 464-8601, JapanThe School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, JapanDepartment of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya 464-8601, JapanRobot Division, System Department, NACHI-FUJIKOSHI CORP., Toyama 930-8511, JapanDepartment of Micro-Nano Mechanical Science and Engineering, Nagoya University, Nagoya 464-8601, JapanTemplate matching is a common approach in bin-picking tasks. However, it often struggles in complex environments, such as those with different object poses, various background appearances, and varying lighting conditions, due to the limited feature representation of a single template. Additionally, during the bin-picking process, the template needs to be frequently updated to maintain detection performance, and finding an adaptive template from a vast dataset poses another challenge. To address these challenges, we propose a novel template searching method in a latent space trained by a Variational Auto-Encoder (VAE), which generates an adaptive template dynamically based on the current environment. The proposed method was evaluated experimentally under various conditions, and in all scenarios, it successfully completed the tasks, demonstrating its effectiveness and robustness for bin-picking applications. Furthermore, we integrated our proposed method with YOLO, and the experimental results indicate that our method effectively improves YOLO’s detection performance.https://www.mdpi.com/1424-8220/24/18/6050template matchingtemplate searchingtemplate generationbin picking |
| spellingShingle | Songtao Liu Yaonan Zhu Tadayoshi Aoyama Masayuki Nakaya Yasuhisa Hasegawa Latent Space Search-Based Adaptive Template Generation for Enhanced Object Detection in Bin-Picking Applications Sensors template matching template searching template generation bin picking |
| title | Latent Space Search-Based Adaptive Template Generation for Enhanced Object Detection in Bin-Picking Applications |
| title_full | Latent Space Search-Based Adaptive Template Generation for Enhanced Object Detection in Bin-Picking Applications |
| title_fullStr | Latent Space Search-Based Adaptive Template Generation for Enhanced Object Detection in Bin-Picking Applications |
| title_full_unstemmed | Latent Space Search-Based Adaptive Template Generation for Enhanced Object Detection in Bin-Picking Applications |
| title_short | Latent Space Search-Based Adaptive Template Generation for Enhanced Object Detection in Bin-Picking Applications |
| title_sort | latent space search based adaptive template generation for enhanced object detection in bin picking applications |
| topic | template matching template searching template generation bin picking |
| url | https://www.mdpi.com/1424-8220/24/18/6050 |
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