  In the field of waste copper granules recycling, engineers should be able to
identify all different sorts of impurities in waste copper granules and
estimate their mass proportion relying on experience before rating. This manual
rating method is costly, lacking in objectivity and comprehensiveness. To
tackle this problem, we propose a waste copper granules rating system based on
machine vision and deep learning. We firstly formulate the rating task into a
2D image recognition and purity regression task. Then we design a two-stage
convolutional rating network to compute the mass purity and rating level of
waste copper granules. Our rating network includes a segmentation network and a
purity regression network, which respectively calculate the semantic
segmentation heatmaps and purity results of the waste copper granules. After
training the rating network on the augmented datasets, experiments on real
waste copper granules demonstrate the effectiveness and superiority of the
proposed network. Specifically, our system is superior to the manual method in
terms of accuracy, effectiveness, robustness, and objectivity.
