论文标题
基于机器视觉的废铜颗粒评级系统
A Waste Copper Granules Rating System Based on Machine Vision
论文作者
论文摘要
在废铜颗粒回收的领域，工程师应该能够识别废物铜颗粒的所有不同种类的杂质，并估计其在评级之前依赖经验的大量比例。这种手动评级方法是昂贵的，缺乏客观性和全面性。为了解决这个问题，我们建议基于机器视觉和深度学习的废铜颗粒评级系统。我们首先将评级任务提出为2D图像识别和纯度回归任务。然后，我们设计了一个两阶段的卷积等级网络，以计算废物铜颗粒的质量纯度和评级水平。我们的评分网络包括分割网络和一个纯度回归网络，该网络分别计算了废物铜颗粒的语义分割热图和纯度结果。在训练增强数据集上的评级网络之后，对真正的废铜颗粒进行了实验，证明了拟议网络的有效性和优势。具体而言，就准确性，有效性，鲁棒性和客观性而言，我们的系统优于手动方法。
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.
