This paper is a part of Journal of Robotics, Networking and Artificial Life (JRNAL). You can access this page for detail information about this publication or download the PDF file. Please note all the contents on this page cannot be used for commerical usage!

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.