用自建数据集训练模型，让机器人自动分拣电子垃圾
Image Segmentation and Classification of E-waste for Training Robots for Waste Segregation
- 自拍电子垃圾零件，构建专用数据集
- YOLOv11实现实时识别，70 mAP表现优异
- 可对接机械臂，适合智能回收场景
工业合作伙伴提出一个实际问题：利用机器学习模型对电子垃圾进行分类，供抓取放置机器人完成垃圾分类。研究团队收集常见电子废弃物（如鼠标、充电器），拆解后拍摄图像，构建了一个定制化数据集。随后使用先进的YOLOv11模型进行训练，在实时推理条件下达到70 mAP的准确率；同时训练的Mask-RCNN模型也获得了41 mAP的性能。该模型可直接集成至抓取放置机器人系统中，实现电子垃圾的自动化分拣。
原文摘要 · Abstract (English)
Industry partners provided a problem statement that involves classifying electronic waste using machine learning models that will be used by pick-and-place robots for waste segregation. This was achieved by taking common electronic waste items, such as a mouse and charger, unsoldering them, and taking pictures to create a custom dataset. Then state-of-the art YOLOv11 model was trained and run to achieve 70 mAP in real-time. Mask-RCNN model was also trained and achieved 41 mAP. The model can be integrated with pick-and-place robots to perform segregation of e-waste.
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。