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This study evaluates the effectiveness of One-Vs-All (OvA) and One-Vs-Rest (OvR) classification strategies for automated waste sorting in Germany, addressing the complexities of local waste disposal regulations. By utilizing a dataset tailored to the waste sorting scheme of Goslar, the research identifies how varying confidence thresholds can optimize the balance between misclassifications and human review efforts. The findings reveal that confidence-guided approaches can significantly enhance the accuracy of waste sorting systems, thereby supporting the transition to a Circular Economy.
Confidence-guided classification can drastically reduce misclassifications in automated waste sorting, enhancing compliance with local disposal regulations.
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.