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Recognition of Plastic Bottles Region Using Improved DeepLab v3+

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Titel: Recognition of Plastic Bottles Region Using Improved DeepLab v3+
Autoren: Murata, Yusuke, Kamiya, Tohru, 402, 神谷, 亨, 80295005, 55739611300, 25
Verlagsinformationen: ALife Robotics
Publikationsjahr: 2025
Bestand: Kyushu Institute of Technology Academic Repository (Kyutacar) / 九州工業大学学術機関リポジトリ
Schlagwörter: Deep Learning, Semantic Segmentation, Convolutional Neural Network (CNN), DeepLab v3+, Efficient Channel Attention Block (ECA Block), Mish function
Beschreibung: Factory automation is one solution to the labor shortage. We focus on the sorting of plastic bottles in waste disposal plants and try to automate the process using robotic arms. In this paper, we propose an image analysis method for the recognition of plastic bottles limited to 500ml capacity. The method is semantic segmentation, and the deep learning model is DeepLab v3+. Modifications using ECA Block and Mish function show improvements at the points of misrecognition with the base model. ; conference paper
Publikationsart: other/unknown material
Dateibeschreibung: application/pdf
Sprache: English
Relation: Proceedings of International Conference on Artificial Life & Robotics (ICAROB2025); 825; 828; https://kyutech.repo.nii.ac.jp/record/2001637/files/10450823.pdf; https://hdl.handle.net/10228/0002001637; https://kyutech.repo.nii.ac.jp/records/2001637
Verfügbarkeit: https://kyutech.repo.nii.ac.jp/record/2001637/files/10450823.pdf
https://hdl.handle.net/10228/0002001637
https://kyutech.repo.nii.ac.jp/records/2001637
Rights: Copyright (c) The 2025 International Conference on Artificial Life and Robotics (ICAROB2025), Feb.13-16, J:COM HorutoHall, Oita, Japan
Dokumentencode: edsbas.6C4A44C6
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  Data: Recognition of Plastic Bottles Region Using Improved DeepLab v3+
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  Data: <searchLink fieldCode="AR" term="%22Murata%2C+Yusuke%22">Murata, Yusuke</searchLink><br /><searchLink fieldCode="AR" term="%22Kamiya%2C+Tohru%22">Kamiya, Tohru</searchLink><br /><searchLink fieldCode="AR" term="%22402%22">402</searchLink><br /><searchLink fieldCode="AR" term="%22神谷%2C+亨%22">神谷, 亨</searchLink><br /><searchLink fieldCode="AR" term="%2280295005%22">80295005</searchLink><br /><searchLink fieldCode="AR" term="%2255739611300%22">55739611300</searchLink><br /><searchLink fieldCode="AR" term="%2225%22">25</searchLink>
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  Data: ALife Robotics
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  Label: Publication Year
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  Data: 2025
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  Data: Kyushu Institute of Technology Academic Repository (Kyutacar) / 九州工業大学学術機関リポジトリ
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  Data: <searchLink fieldCode="DE" term="%22Deep+Learning%22">Deep Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Semantic+Segmentation%22">Semantic Segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+Neural+Network+%28CNN%29%22">Convolutional Neural Network (CNN)</searchLink><br /><searchLink fieldCode="DE" term="%22DeepLab+v3%2B%22">DeepLab v3+</searchLink><br /><searchLink fieldCode="DE" term="%22Efficient+Channel+Attention+Block+%28ECA+Block%29%22">Efficient Channel Attention Block (ECA Block)</searchLink><br /><searchLink fieldCode="DE" term="%22Mish+function%22">Mish function</searchLink>
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  Label: Description
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  Data: Factory automation is one solution to the labor shortage. We focus on the sorting of plastic bottles in waste disposal plants and try to automate the process using robotic arms. In this paper, we propose an image analysis method for the recognition of plastic bottles limited to 500ml capacity. The method is semantic segmentation, and the deep learning model is DeepLab v3+. Modifications using ECA Block and Mish function show improvements at the points of misrecognition with the base model. ; conference paper
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  Data: Proceedings of International Conference on Artificial Life & Robotics (ICAROB2025); 825; 828; https://kyutech.repo.nii.ac.jp/record/2001637/files/10450823.pdf; https://hdl.handle.net/10228/0002001637; https://kyutech.repo.nii.ac.jp/records/2001637
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  Data: https://kyutech.repo.nii.ac.jp/record/2001637/files/10450823.pdf<br />https://hdl.handle.net/10228/0002001637<br />https://kyutech.repo.nii.ac.jp/records/2001637
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  Data: Copyright (c) The 2025 International Conference on Artificial Life and Robotics (ICAROB2025), Feb.13-16, J:COM HorutoHall, Oita, Japan
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      – Text: English
    Subjects:
      – SubjectFull: Deep Learning
        Type: general
      – SubjectFull: Semantic Segmentation
        Type: general
      – SubjectFull: Convolutional Neural Network (CNN)
        Type: general
      – SubjectFull: DeepLab v3+
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      – SubjectFull: Efficient Channel Attention Block (ECA Block)
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      – SubjectFull: Mish function
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      – TitleFull: Recognition of Plastic Bottles Region Using Improved DeepLab v3+
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            NameFull: Murata, Yusuke
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            NameFull: Kamiya, Tohru
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              Type: published
              Y: 2025
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