Problem
Research questions and friction points this paper is trying to address.
irregular component segmentation
e-waste disassembly
robotic recycling
vision models
material recovery
Evaluating Large and Lightweight Vision Models for Irregular Component Segmentation in E-Waste Disassembly
🤖 AI Summary
This study addresses the challenge of accurately segmenting irregular and densely packed components in electronic waste by presenting the first systematic comparison between the general-purpose foundation model SAM2 and the lightweight task-specific model YOLOv8. Leveraging a newly curated dataset of 1,456 high-quality annotated images and diverse data augmentation strategies, experiments demonstrate that YOLOv8 significantly outperforms SAM2, achieving an mAP50 of 98.8% and an mAP50–95 of 85%, with notably more precise boundary delineation. Although SAM2 offers greater structural flexibility, it suffers from mask overlap and contour inconsistency issues. The findings underscore that off-the-shelf vision foundation models require targeted adaptation to suit industrial recycling applications. The authors publicly release the dataset and benchmark framework to support further research in this domain.
📝 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 large pre-trained models require task-specific optimization for industrial applications. The resulting dataset and benchmarking framework provide a foundation for developing scalable vision algorithms for robotic e-waste disassembly and circular manufacturing systems.
Innovation
Methods, ideas, or system contributions that make the work stand out.
irregular component segmentation
e-waste disassembly
YOLOv8
SAM2
vision benchmarking
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