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

2026-07-02

AI Summary

This study addresses the significant variation in municipal waste sorting regulations across Germany, which complicates residents’ compliance and hinders circular economy development. Focusing on the waste classification system in Goslar, the authors propose a human-in-the-loop classification framework that integrates One-vs-All and One-vs-Rest strategies. The approach employs a confidence-guided mechanism to dynamically identify low-confidence predictions and route them for manual verification, thereby reducing automated misclassification while effectively constraining human annotation costs. Experimental results demonstrate that the system accurately flags error-prone samples and substantially improves overall classification accuracy with minimal human intervention, offering a practical, configurable, and locally adaptable solution for intelligent waste sorting.

Abstract

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.

Problem

• waste sorting
• Circular Economy
• misclassification
• municipal waste
• human-in-the-loop

Innovation

• confidence-guided classification
• human-in-the-loop
• waste sorting
• One-Vs-All
• One-Vs-Rest