用深度学习自动分类医疗垃圾，贴合尼泊尔垃圾分类标准
Health Care Waste Classification Using Deep Learning Aligned with Nepal's Bin Color Guidelines
- 用5种主流模型在混合数据上对比分类效果
- YOLOv5-s准确率达95.06%，推理速度优于EfficientNet-B0
- 结果已部署网页系统，按尼泊尔垃圾桶颜色标准标注
尼泊尔医疗设施增多，医疗废物管理压力上升。不当分拣与处理易导致污染、传染病传播及工作人员风险。本研究在整合的医疗废物数据集上，采用分层5折交叉验证，对比了ResNeXt-50、EfficientNet-B0、MobileNetV3-S、YOLOv8-n和YOLOv5-s五种先进分类模型。结果显示，YOLOv5-s达到最高准确率95.06%，但推理速度略慢于YOLOv8-n（仅差几毫秒）；EfficientNet-B0准确率为93.22%，但推理时间最长。经重复ANOVA检验确认结果显著性后，将表现最佳的YOLOv5-s模型部署至网页系统，并依据尼泊尔医疗废物管理标准，对分类结果按垃圾桶颜色进行映射。未来工作建议解决数据局限性并增强本地化适配。
原文摘要 · Abstract (English)
The increasing number of Health Care facilities in Nepal has added up the challenges on managing health care waste (HCW). Improper segregation and disposal of HCW leads to contamination, spreading of infectious diseases and risk for waste handlers. This study benchmarks the state of the art waste classification models: ResNeXt-50, EfficientNet-B0, MobileNetV3-S, YOLOv8-n and YOLOv5-s using stratified 5-fold cross-validation technique on combined HCW data. YOLOv5-s achieved the highest accuracy (95.06%) but fell short with the YOLOv8-n model in inference speed with few milliseconds. The EfficientNet-B0 showed promising results of 93.22% accuracy but took the highest inference time. Following a repetitive ANOVA test to confirm the statistical significance, the best performing model (YOLOv5-s) was deployed to the web with bin color mapped using Nepal's HCW management standards. Further work is suggested to address data limitation and ensure localized context.
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