Evaluating Large and Lightweight Vision Models for Irregular Component Segmentation in E-Waste Disassembly
📰 ArXiv cs.AI
Evaluating large and lightweight vision models for irregular component segmentation in e-waste disassembly
Action Steps
- Collect and annotate a dataset of images of e-waste components
- Train and test large vision models like SAM2 and lightweight models like YOLOv8 on the dataset
- Compare the performance of the models in terms of segmentation accuracy and efficiency
- Select the most suitable model based on the trade-off between accuracy and computational resources
Who Needs to Know This
Computer vision engineers and researchers on a team can benefit from this study to improve e-waste recycling processes, and product managers can use these findings to inform decisions on model selection and development
Key Insight
💡 The choice of vision model architecture and scale significantly impacts segmentation performance in e-waste disassembly
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💡 Evaluating vision models for e-waste disassembly: large vs lightweight #AI #ComputerVision
Key Takeaways
Evaluating large and lightweight vision models for irregular component segmentation in e-waste disassembly
Full Article
Title: Evaluating Large and Lightweight Vision Models for Irregular Component Segmentation in E-Waste Disassembly
Abstract:
arXiv:2603.27441v1 Announce Type: cross 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 lapt
Abstract:
arXiv:2603.27441v1 Announce Type: cross 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 lapt
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