轻量模型YOLOv8在电子垃圾拆解组件分割上表现优于大模型SAM2。
Evaluating Large and Lightweight Vision Models for Irregular Component Segmentation in E-Waste Disassembly
- 用YOLOv8和SAM2对比，测试不同模型在复杂电子元件分割中的表现。
- YOLOv8达到mAP50=98.8%、mAP50-95=85%，远超SAM2的8.4%。
- 研究提供新数据集与基准框架，适合机器人拆解与循环经济系统研发者。
精确分割不规则且密集排列的电子元件对电子垃圾回收中的机器人拆解与材料回收至关重要。本研究通过对比基于Transformer的SAM2与轻量级YOLOv8网络，评估模型架构与规模对分割性能的影响。两种模型均在新收集的1,456张带标注的笔记本电脑组件RGB图像数据集上训练与测试，涵盖逻辑板、散热片和风扇等部件，覆盖不同光照与姿态条件。采用随机旋转、翻转和裁剪等数据增强技术提升模型鲁棒性。实验结果显示，YOLOv8在分割准确率（mAP50 = 98.8%，mAP50-95 = 85%）和边界精度方面显著优于SAM2（mAP50 = 8.4%）。SAM2虽能灵活表示多样结构，但常产生重叠掩码与不一致轮廓。结果表明，大模型需任务定制优化才适用于工业场景。研究提供的数据集与基准框架为开发可扩展的视觉算法支持机器人电子垃圾拆解与循环制造系统奠定了基础。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。