这篇论文用德国戈斯拉尔的真实垃圾分类数据，比较了OvA和OvR两种策略，还加入了人工检查的环节，减少误分类的同时控制成本，挺实用的。
该研究比较了One-Vs-All和One-Vs-Rest两种分类策略在德国戈斯拉尔市垃圾分类数据集上的表现，数据集包含该市特定的垃圾分类方案。通过设置不同置信度阈值，识别可能误分类的样本并引入人工审核，旨在平衡误分类率与人工标注成本。实验结果表明，OvR策略在较低阈值下能更有效地减少误分类。该工作为定制化AI垃圾分类应用提供了参考。
Efficient Waste Sorting for Circular Economy: A Confidence-guided comparison between One-Vs-All and One-Vs-Rest Classification Strategies with Human-in-the-Loop for Automated Waste Sorting
The complexity of waste disposal regulations across European countries poses significant challenges for the residents and hinders the transition to a Circular Economy. In Germany, the proper sorting and disposal of household waste remains challenging across municipalities. Consequently, substantially reducing incorrectly disposed waste is vital for improving waste management and advancing the Circular Economy. AI-based waste sorting solutions can support residents through user-friendly tools, such as mobile applications, that guide proper waste disposal. To be effective in supporting the Circular Economy, however, these solutions must be configurable to reflect the specific waste sorting scheme of individual municipalities in Germany. In the scope of this work, an evaluation and analysis are performed of two prominent classification strategies: OvA and OvR. The research uses a dataset constructed in alignment with the waste categories and sorting scheme of the city of Goslar in Germany. Moreover, this work aims to extend beyond the overall performance by examining the behavior of OvA and OvR classification strategies in identifying samples likely to be misclassified. These classification strategies are compared by applying varying confidence thresholds to identify uncertain samples for subsequent human review. This evaluation aims to balance the number of misclassifications against the human effort required for data annotation.