Vukicevic Arso M, Petrovic Milos, Jurisevic Nebojsa, Djapan Marko, Knezevic Nikola, Novakovic Aleksandar, Jovanovic Kosta
Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, Kragujevac, Serbia.
School of Mathematics and Physics, Queen's University Belfast, University Road, Belfast, Northern Ireland, BT7 1NN, UK.
Sci Rep. 2025 Jan 30;15(1):3756. doi: 10.1038/s41598-025-87226-x.
The expansion of LEAN and small batch manufacturing demands flexible automated workstations capable of switching between sorting various wastes over time. To address this challenge, our study is focused on assessing the ability of the Segment Anything Model (SAM) family of deep learning architectures to separate highly variable objects during robotic waste sorting. The proposed two-step procedure for generic versatile visual waste sorting is based on the SAM architectures (original SAM, FastSAM, MobileSAMv2, and EfficientSAM) for waste object extraction from raw images, and the use of classification architecture (MobileNetV2, VGG19, Dense-Net, Squeeze-Net, ResNet, and Inception-v3) for accurate waste sorting. Such a pipeline brings two key advantages that make it more applicable in industry practice by: 1) eliminating the necessity for developing dedicated waste detection and segmentation algorithms for waste object localization, and 2) significantly reducing the time and costs required for adapting the solution to different use cases. With the proposed procedure, switching to a new waste type sorting is reduced to only two steps: The use of SAM for the automatic object extraction, followed by their separation into corresponding classes used to fine-tune the classifier. Validation on four use cases (floating waste, municipal waste, e-waste, and smart bins) shows robust results, with accuracy ranging from 86 to 97% when using the MobileNetV2 with SAM and FastSAM architectures. The proposed approach has a high potential to facilitate deployment, increase productivity, lower expenses, and minimize errors in robotic waste sorting while enhancing overall recycling and material utilization in the manufacturing industry.
精益生产和小批量制造的扩展需要灵活的自动化工作站，能够随着时间的推移在分拣各种废物之间进行切换。为应对这一挑战，我们的研究专注于评估深度学习架构的“分割一切模型”（SAM）家族在机器人废物分拣过程中分离高度可变物体的能力。所提出的用于通用视觉废物分拣的两步程序基于SAM架构（原始SAM、FastSAM、MobileSAMv2和EfficientSAM）从原始图像中提取废物对象，并使用分类架构（MobileNetV2、VGG19、Dense-Net、Squeeze-Net、ResNet和Inception-v3）进行精确的废物分拣。这样的流程带来了两个关键优势，使其在工业实践中更具适用性：1）无需为废物对象定位开发专门的废物检测和分割算法；2）显著减少将解决方案应用于不同用例所需的时间和成本。通过所提出的程序，切换到新的废物类型分拣只需两步：使用SAM进行自动对象提取，然后将它们分成相应的类别以微调分类器。在四个用例（漂浮废物、城市垃圾、电子废物和智能垃圾桶）上的验证显示了稳健的结果，当使用带有SAM和FastSAM架构的MobileNetV2时，准确率在86%至97%之间。所提出的方法在促进机器人废物分拣的部署、提高生产率、降低成本以及最大限度减少错误方面具有很大潜力，同时还能提高制造业的整体回收和材料利用率。
