| Title: | GS2-4 Image Processing for Picking Task of Random Ordered PET Drinking Bottles |
|---|---|
| Publication: | ICAROB2019 |
| Volume: | 24 |
| Pages: | 634-637 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2019.GS2-4 |
| Author(s): | Chen Zhu, Takafumi Matsumaru |
| Publication Date: | January 10, 2019 |
| Keywords: | Image Processing, Robotics Picking, Deep Learning, COCO Dataset |
| Abstract: | In this research, six brands of soft drinks are decided to be picked up by a robot with a monocular RGB camera. The drinking bottles need to be located and classified with brands before being picked up. A Mask R-CNN is pretrained with COCO datasets to detect and generate the mask on the bottles in the image. The Inception v3 is selected for the brand classification task. Around 200 images are taken, then, the images are augmented to 1500 images per brand by using random cropping and perspective transform. The results show that the masked image can be labeled with its brand name with at least 85% accuracy in the experiment. |
| PDF File: | https://alife-robotics.co.jp/members2019/icarob/data/html/data/GS_pdf/GS2/GS2-4.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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