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This thesis develops and deploys a YOLO-based vision system to automatically identify and sort five waste categories: metal, cardboard, glass, paper, and plastic. The work evaluates model performance pre-deployment and under deployment, then measures single-arm reliability in fixed and conveyor workspaces and validates dual-arm efficiency and safety. Precision decreases after deployment but robotic grasping remains highly reliable when objects are detected, highlighting practical low-cost operation and vision-only limitations, with improvement directions proposed.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-driven-waste-sorting-with-robotic-arms-degree-thesis/128848/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-driven-waste-sorting-with-robotic-arms-degree-thesis/128848.png","ImageObject",300,407,{"name":92,"@type":93},"Aria","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-19","2026-08-06",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What waste categories does the proposed system sort?","Question",{"text":113,"@type":114},"The YOLO-based system is designed to identify and sort five waste categories: metal, cardboard, glass, paper, and plastic.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the YOLO model’s precision change after deployment?",{"text":118,"@type":114},"Precision reaches 92.7% before deployment (test set). After deployment, it drops to 70.9% in the fixed workspace and 65.5% on the conveyor belt under static camera views.",{"name":120,"@type":111,"acceptedAnswer":121},"Why is robotic grasping still reliable despite lower detection performance?",{"text":122,"@type":114},"The evaluation shows that while object detection declines in real-world conditions—especially on dark conveyor belts—robotic grasping remains highly reliable once the target object is successfully identified.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},128848,1786003863,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145},2336474459895,"https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916","Machine learning-driven waste sorting with robotic arms  \nQiongxia Qu  \nDegree Thesis  \nMaterials Processing Technology 2025  \nDegree Thesis  \nQiongxia Qu  \nMachine learning-driven waste sorting with robotic arms  \nArcada University of Applied Sciences: Materials Processing Technology, 2025.  \nCommissioned by:  \nArcada University of Applied Science  \nAbstract:  \nMachine learning-driven waste sorting systems, integrated with robotic arms, can mitigate the limitations of manual sorting. This study develops and deploys a YOLO-based system for automatic identification and sorting of five waste categories: metal, cardboard, glass, paper, and plastic. A key contribution of this study is the integration of the vision-based detection model with robotic arms, implemented in both single-arm and dual-arm experimental setups. To comprehensively evaluate the performance of the purposed system, this study designed a progressive five-layer evaluation framework: from pre-deployment performance of the YOLO model (layer 1) and its adaptability after deployment(layer 2), to the sorting reliability of the single-arm system in both fixed workspace(layer 3) and conveyor workspace(layer 4), and finally the efficiency and safety validation of the dual-arm system(layer 5) . The YOLO model achieved 92.7% precision on the test set before deployment (layer 1) . After deployment, precision under static camera views of the robotic arms dropped to 70.9% in the fixed workspace and 65.5% on the conveyor belt (layer 2); single-arm overall success rates were 76.7%(fixed workspace, layer 3) and 63.3%(conveyor workspace, layer 4), and the dual-arm system handled 40% of samples (layer 5), all with 100% picking success for correctly detected objects. The evaluation results show that although object detection performance declines under real-world conditions, especially on dark conveyor belts, robotic grasping remains highly reliable once targets are successfully identified. This confirms that low-cost robotic arms possess stable physical operational capabilities in practica sorting tasks. The study further highlights the limitations of vision-only classification under cost constraints. Given that many small and medium-sized recycling facilities cannot afford industrial-grade equipment, exploring the feasibility and limitations of low-cost solutions holds significant practical  \nrelevance. Finally, this study proposes future directions for improvement, including enhancing dataset diversity, integrating multi-sensor, and optimizing dual-arm coordination strategies, with the aim of further improving system efficiency and robustness, thereby providing a reference for promoting economically viable robotic sorting solutions in industrial applications.  \nKeywords: YOLO, Waste sorting; Computer vision; Object detection; Robotic arm  \nIntegration; Dual arm synchronization  \nContents  \n1 Introduction...................................................................................................................................9  \n1.1 Background .......................................................................................................................................... 9  \n1.2 Problem statement ............................................................................................................................ 10  \n1.3 Aims of the study ............................................................................................................................... 12  \n1.4 Compliance with the degree programme theme .............................................................................. 12  \n2 Literature review ......................................................................................................................... 12  \n2.1 Machine Learning .............................................................................................................................. 13  \n2.1.1 Supervised learning ........................................","cbCaiqRa8W4BHoYZ","https://ap.wps.com/l/cbCaiqRa8W4BHoYZ","pdf",3182487,102,"English","# Introduction\n## Background\n## Problem statement\n## Aims of the study\n## Compliance with the degree programme theme\n# Literature review\n## Machine Learning\n## Computer vision\n## Success metrics\n## AI-based sorting\n# Method","[{\"question\":\"What waste categories does the proposed system sort?\",\"answer\":\"The YOLO-based system is designed to identify and sort five waste categories: metal, cardboard, glass, paper, and plastic.\"},{\"question\":\"How does the YOLO model’s precision change after deployment?\",\"answer\":\"Precision reaches 92.7% before deployment (test set). After deployment, it drops to 70.9% in the fixed workspace and 65.5% on the conveyor belt under static camera views.\"},{\"question\":\"Why is robotic grasping still reliable despite lower detection performance?\",\"answer\":\"The evaluation shows that while object detection declines in real-world conditions—especially on dark conveyor belts—robotic grasping remains highly reliable once the target object is successfully identified.\"}]","Machine learning-driven waste sorting with robotic arms - Degree Thesis | PDF",257]