The use of robots in waste processing plants can significantly improve the processing of recyclables. Such robots need sophisticated visual and manipulation skills to be able to work in the extremely heterogeneous, complex, and unpredictable waste sorting industrial environment. This article considers the implementation of an autonomous robotic system for the categorization and physical sorting of recyclables according to material types. In particular, it focuses on the development of a low-cost computer vision module based on deep learning technologies to identify and sort items. To facilitate further research endeavors, the data set of recyclable images and a group of image processing scripts for object identification, masking, and synthetic placement against multiple backgrounds are available in an open source GitHub repository (https://github.com/kskmar/ReSort-IT.git). The deep-trained computer vision module is integrated with a robotic system that undertakes the physical separation of recyclables. The composite system is deployed in a waste processing plant, where it is successfully assessed in recyclable sorting under difficult and demanding industrial conditions.

中文翻译：

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机器人垃圾分类技术：面向可回收垃圾工业机器人分离的基于视觉的分类系统


在废物处理厂中使用机器人可以显着改善可回收物的处理。此类机器人需要复杂的视觉和操纵技能，才能在极其异构、复杂且不可预测的废物分类工业环境中工作。本文考虑实施一个自主机器人系统，用于根据材料类型对可回收物进行分类和物理分类。特别是，它专注于开发基于深度学习技术的低成本计算机视觉模块来识别和分类物品。为了促进进一步的研究工作，可回收图像的数据集和一组用于针对多个背景进行对象识别、屏蔽和合成放置的图像处理脚本可在开源 GitHub 存储库 (https://github.com/kskmar/重新排序-IT.git）。经过深度训练的计算机视觉模块与机器人系统集成，负责对可回收物进行物理分离。该复合系统部署在废物处理厂中，并在困难和苛刻的工业条件下成功进行了可回收分类评估。