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

1. 1Collect and annotate a dataset of images of e-waste components
2. 2Train and test large vision models like SAM2 and lightweight models like YOLOv8 on the dataset
3. 3Compare the performance of the models in terms of segmentation accuracy and efficiency
4. 4Select the most suitable model based on the trade-off between accuracy and computational resources

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

💡 The choice of vision model architecture and scale significantly impacts segmentation performance in e-waste disassembly

💡 Evaluating vision models for e-waste disassembly: large vs lightweight #AI #ComputerVision