| Title: | GS2-1 Recognition of Plastic Bottles Region Using Improved DeepLab v3+ |
|---|---|
| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 825-828 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.GS2-1 |
| Author(s): | Yusuke Murata, Tohru Kamiya |
| Publication Date: | February 13, 2025 |
| Keywords: | Deep Learning, Semantic Segmentation, Convolutional Neural Network (CNN), DeepLab v3+, Efficient Channel Attention Block (ECA Block), Mish function |
| Abstract: | 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. |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/GS/GS2/GS2-1.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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