用深度学习精准识别塑料垃圾，YOLO模型表现最佳。
Plastic Waste Classification Using Deep Learning: Insights from the WaDaBa Dataset
- 采用YOLO系列模型与CNN处理塑料垃圾图像分类。
- YOLO-11m准确率达98.03%，mAP50达0.990，性能最优。
- 轻量模型适合快速训练，适合边缘部署场景。
随着塑料使用量增加，塑料废物管理挑战日益严峻，亟需高效分类与回收方案。本研究基于WaDaBa数据集，探索卷积神经网络（CNN）与目标检测模型（如YOLO）在塑料垃圾分类中的应用。结果表明，YOLO-11m在准确率（98.03%）和mAP50（0.990）上表现最佳；YOLO-11n虽略低但mAP50高达0.992。轻量级模型YOLO-10n训练更快但精度较低；MobileNet V2在分类任务中达到97.12%准确率，但在目标检测上表现不足。研究表明，深度学习可显著提升塑料垃圾分类效率，其中YOLO模型兼具高精度与计算效率，具备规模化部署潜力，为废物管理与回收提供可行技术路径。
原文摘要 · Abstract (English)
With the increasing use of plastic, the challenges associated with managing plastic waste have become more challenging, emphasizing the need of effective solutions for classification and recycling. This study explores the potential of deep learning, focusing on convolutional neural networks (CNNs) and object detection models like YOLO (You Only Look Once), to tackle this issue using the WaDaBa dataset. The study shows that YOLO- 11m achieved highest accuracy (98.03%) and mAP50 (0.990), with YOLO-11n performing similarly but highest mAP50(0.992). Lightweight models like YOLO-10n trained faster but with lower accuracy, whereas MobileNet V2 showed impressive performance (97.12% accuracy) but fell short in object detection. Our study highlights the potential of deep learning models in transforming how we classify plastic waste, with YOLO models proving to be the most effective. By balancing accuracy and computational efficiency, these models can help to create scalable, impactful solutions in waste management and recycling.
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