A Waste Copper Granules Rating System Based on Machine Vision
This addresses the costly and subjective manual rating process in waste copper recycling, representing an incremental improvement by applying existing deep learning techniques to a specific industrial domain.
The paper tackles the problem of manual rating of waste copper granules by proposing a machine vision and deep learning system that formulates it as a 2D image recognition and purity regression task, achieving superiority over manual methods in accuracy, effectiveness, robustness, and objectivity.
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.