[2603.27441] Evaluating Large and Lightweight Vision Models for Irregular Component Segmentation in E-Waste Disassembly

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.27441 (cs) [Submitted on 28 Mar 2026] Title:Evaluating Large and Lightweight Vision Models for Irregular Component Segmentation in E-Waste Disassembly Authors:Xinyao Zhang, Chang Liu, Xiao Liang, Minghui Zheng, Sara Behdad View a PDF of the paper titled Evaluating Large and Lightweight Vision Models for Irregular Component Segmentation in E-Waste Disassembly, by Xinyao Zhang and 4 other authors View PDF HTML (experimental) Abstract: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. Both models were trained and tested on a newly collected dataset of 1,456 annotated RGB images of laptop components including logic boards, heat sinks, and fans, captured under varying illumination and orientation conditions. Data augmentation techniques, such as random rotation, flipping, and cropping, were applied to improve model robustness. YOLOv8 achieved higher segmentation accuracy (mAP50 = 98.8%, mAP50-95 = 85%) and stronger boundary precision than SAM2 (mAP50 = 8.4%). SAM2 demonstrated flexibility in representing diverse object structures but often produced overlapping masks and inconsistent contours. These findings show that ...