# A Waste Copper Granules Rating System Based on Machine Vision

arXiv: https://arxiv.org/abs/2207.04575 · HTML: https://mlsanity.com/paper/2207.04575/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 41 references that are in the map.
- First posted on arXiv 11 Jul 2022.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [CGNet: A Light-weight Context Guided Network for Semantic Segmentation](https://mlsanity.com/paper/1811.08201/) (2018)
- [Small Obstacle Avoidance Based on RGB-D Semantic Segmentation](https://mlsanity.com/paper/1908.11675/) (2019)
- [Deep Global-Relative Networks for End-to-End 6-DoF Visual Localization and Odometry](https://mlsanity.com/paper/1812.07869/) (2018)
- [Facial Pose Estimation by Deep Learning from Label Distributions](https://mlsanity.com/paper/1904.13102/) (2019)
- [Deep Learning Based Robot for Automatically Picking up Garbage on the Grass](https://mlsanity.com/paper/1904.13034/) (2019)
- [Wearable Travel Aid for Environment Perception and Navigation of Visually Impaired People](https://mlsanity.com/paper/1904.13037/) (2019)
- [A Realistic Face-to-Face Conversation System based on Deep Neural Networks](https://mlsanity.com/paper/1908.07750/) (2019)
- [Video synthesis of human upper body with realistic face](https://mlsanity.com/paper/1908.06607/) (2019)
- [How Old Are You? Face Age Translation with Identity Preservation Using GANs](https://mlsanity.com/paper/1909.04988/) (2019)
- [A Neural Virtual Anchor Synthesizer based on Seq2Seq and GAN Models](https://mlsanity.com/paper/1908.07262/) (2019)

## Abstract (arXiv)

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.