A Waste Copper Granules Rating System Based on Machine Vision

TL;DR

This paper introduces a machine vision and deep learning-based system for objectively rating waste copper granules, replacing manual methods with higher accuracy and robustness.

Contribution

It formulates the rating as a 2D image recognition and purity regression task and designs a two-stage convolutional network for improved assessment.

Findings

Outperforms manual rating in accuracy

Demonstrates robustness and effectiveness

Validates on real waste copper samples

Abstract

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.