Using Style-Transfer to Understand Material Classification for Robotic Sorting of Recycled Beverage Containers

Abstract

Robotic sorting machines are increasingly being investigated for use in recycling centers. We consider the problem of automatically classifying images of recycled beverage containers by material type, i.e. glass, plastic, metal or liquid-packaging-board, when the containers are not in their original condition, meaning their shape and size may be deformed, and coloring and packaging labels may be damaged or dirty. We describe a retrofitted computer vision system and deep convolutional neural network classifier designed for this purpose, that enabled a sorting machine's accuracy and speed to reach commercially viable benchmarks. We investigate what was more important for highly accurate container material recognition: shape, size, color, texture or all of these? To help answer this question, we made use of style-transfer methods from the field of deep learning. We found that removing either texture or shape cues significantly reduced the accuracy in container material classification, while removing color had a minor negative effect. Unlike recent work on generic objects in ImageNet, networks trained to classify by container material type learned better from object shape than texture. Our findings show that commercial sorting of recycled beverage containers by material type at high accuracy is feasible, even when the containers are in poor condition. Furthermore, we reinforce the recent finding that convolutional neural networks can learn predominantly either from texture cues or shape.