AI RESEARCH
Evaluating Large and Lightweight Vision Models for Irregular Component Segmentation in E-Waste Disassembly
arXiv CS.AI
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ArXi:2603.27441v1 Announce Type: cross Precise segmentation of irregular and densely arranged components is essential for robotic disassembly and material recovery in electronic waste (e-waste) recycling. This study evaluates the impact of model architecture and scale on segmentation performance by comparing SAM2, a transformer-based vision model, with the lightweight YOLOv8 network.