虚拟矿山——利用深度学习对印刷电路板进行组件级回收
Virtual Mines -- Component-level recycling of printed circuit boards using deep learning
AI总结：
本文提出利用深度学习（YOLOv5）实现印刷电路板组件级回收，以应对电子废弃物增长问题，并在本地数据集上取得了良好的精确率和召回率。
摘要：
本文概述了一个正在进行的项目，该项目利用机器学习和计算机视觉组件来改进电子废弃物回收过程。在循环经济中，“虚拟矿山”概念指的是从报废物品中高效且经济地回收有价值原材料的生产循环。特别是，由于高科技产品生命周期日益缩短，电子废弃物的增长是一个全球性问题。本文描述了一个基于深度学习模型的流水线，用于在组件级别回收印刷电路板。使用预训练的YOLOv5模型来分析本地开发数据集的结果。在不同类别实例分布的情况下，YOLOv5成功实现了令人满意的精确率和召回率，并具有通过大型组件实例进行优化的能力。
英文摘要：
This contribution gives an overview of an ongoing project using machine learning and computer vision components for improving the electronic waste recycling process. In circular economy, the "virtual mines" concept refers to production cycles where interesting raw materials are reclaimed in an efficient and cost-effective manner from end-of-life items. In particular, the growth of e-waste, due to the increasingly shorter life cycle of hi-tech goods, is a global problem. In this paper, we describe a pipeline based on deep learning model to recycle printed circuit boards at the component level. A pre-trained YOLOv5 model is used to analyze the results of the locally developed dataset. With a different distribution of class instances, YOLOv5 managed to achieve satisfactory precision and recall, with the ability to optimize with large component instances.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
