Smart & Sustainable Manufacturing Systems Laboratory (SMART Lab), Department of Mechanical Engineering, University of Alberta, 9211 116 Street NW, Edmonton, AB, Canada.
Department of Architecture and Built Environment, Northumbria University, Newcastle upon Tyne, UK.
Waste Manag. 2024 Apr 15;178:144-154. doi: 10.1016/j.wasman.2024.02.028. Epub 2024 Feb 23.
A material recovery facility (MRF) can transform municipal solid waste (MSW) into a valued commodity called refuse-derived fuel (RDF) as a promising solution to waste-to-energy conversion. The quality of the produced RDF significantly relies on the composition of in-feed waste and waste characterization method applied for auditing purposes, a process that is both time-consuming and fraught with potential hazards. This study focuses to enhance the workflow of the waste characterization process at an MRF. A solution named Smart Sight is proposed to detect and classify waste based on videos recorded after processing MSW through a mechanical sorting line consisting of bag breakers and trommel screens. A comprehensive dataset is created encompassing thirteen mixed waste classes from single and multi-family streams. The dataset is preprocessed with motion compensation techniques and frame differencing methods to extract and refine valuable frames. A one-stage YOLO detector model is then trained over the dataset. The experimental results show that the proposed method works efficiently at detecting and classifying waste objects in indoor MRF environments. Accuracy, precision, recall, and F1 score related to the proposed solution are found to be 0.70, 0.762, 0.69 and 0.72, respectively, with a mAP@ of 0.716. The proposed approach is validated using data collected from local MRF by comparing the estimated waste composition values of the proposed solution with laboratory results obtained through current standardized industrial practices. Comparison reveals that waste characterization estimation obtained is consistent with the laboratory results, inferring that Smart-Sight is a viable tool for estimating waste composition.
材料回收设施 (MRF) 可以将城市固体废物 (MSW) 转化为一种有价值的商品，称为垃圾衍生燃料 (RDF)，这是一种有前途的废物转化为能源的解决方案。所生产的 RDF 的质量在很大程度上取决于进料废物的组成和用于审核目的的废物特征化方法，这是一个既耗时又充满潜在危险的过程。本研究旨在增强 MRF 中废物特征化过程的工作流程。提出了一种名为 Smart Sight 的解决方案，该解决方案基于通过由破袋器和滚筒筛组成的机械分拣线处理 MSW 后录制的视频来检测和分类废物。创建了一个包含来自单户和多户家庭的 13 种混合废物类别的综合数据集。使用运动补偿技术和帧差分方法对数据集进行预处理，以提取和精炼有价值的帧。然后在数据集上训练了一个单阶段 YOLO 检测器模型。实验结果表明，该方法在室内 MRF 环境中检测和分类废物物体的效率很高。与所提出的解决方案相关的准确性、精度、召回率和 F1 分数分别为 0.70、0.762、0.69 和 0.72，mAP@ 为 0.716。通过将所提出的解决方案估计的废物成分值与通过当前标准化工业实践获得的实验室结果进行比较，使用从当地 MRF 收集的数据验证了所提出的方法。比较表明，废物特征化估计值与实验室结果一致，这表明 Smart-Sight 是一种估计废物成分的可行工具。
