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

TL;DR

This paper compares transformer-based SAM2 and lightweight YOLOv8 models for segmenting irregular e-waste components, highlighting the importance of task-specific optimization for industrial applications.

Contribution

It introduces a new dataset and benchmarking framework for evaluating vision models in e-waste disassembly tasks.

Findings

YOLOv8 achieved higher segmentation accuracy (mAP50 = 98.8%)

SAM2 showed flexibility but had overlapping masks and inconsistent contours

Large pre-trained models need task-specific tuning for industrial use

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.