Image Processing for Picking Task of Random Ordered PET Drinking Bottles

DOI

10.2991/jrnal.k.190531.008How to use a DOI?

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 Red Green Blue (RGB) camera. The drinking bottles need to be located and classified with brands before being picked up. The Mask Regional Convolutional Neural Network (R-CNN), a mask generation network improved from Faster R-CNN, is trained with common object in contest 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 or found at first; then, the images are augmented to 1500 images per brands by using random cropping and perspective transform. The result shows that the masked image can be labeled with its brand name with at least 85% accuracy in the experiment.

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Chen Zhu
AU  - Takafumi Matsumaru
PY  - 2019
DA  - 2019/06/25
TI  - Image Processing for Picking Task of Random Ordered PET Drinking Bottles
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 38
EP  - 41
VL  - 6
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.190531.008
DO  - 10.2991/jrnal.k.190531.008
ID  - Zhu2019
ER  -