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

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Mohammed Fahad Ali, Dominique Briechle, Marit Briechle-Mathiszig, Tobias Geger, Andreas Rausch
• Venue: arXiv: cs.CV
• Published: 2026-07-02
• Collected: 2026-07-03
• URL: http://arxiv.org/abs/2607.02230v1
• PDF: https://arxiv.org/pdf/2607.02230v1
• DOI: UNKNOWN

Plain-Language Brief

Researchers evaluated and analyzed two waste sorting algorithms, OvA and OvR, using a dataset based on the waste categories and sorting scheme of Goslar, Germany. The goal is to minimize misclassifications while keeping human effort for data annotation at a minimum by applying confidence thresholds.

What the Document Says

• The complexity in waste disposal regulations across European countries hinders the transition to a Circular Economy.
• Proper sorting and disposal of household waste remains challenging in Germany, particularly across municipalities.
• AI-based waste sorting solutions can guide residents through user-friendly tools like mobile applications for correct waste disposal.
• These solutions must be configurable to reflect the specific waste sorting scheme of individual municipalities in Germany.
• The research focuses on evaluating and analyzing two classification strategies: OvA and OvR.
• The evaluation is performed using a dataset constructed based on Goslar's waste categories and sorting scheme.
• The study aims to extend beyond overall performance by examining the behavior of OvA and OvR in identifying samples likely to be misclassified.
• Classification strategies are compared by applying varying confidence thresholds to identify uncertain samples for human review.

Key Concepts Mentioned

• waste disposal
• Circular Economy
• Germany
• municipalities
• AI-based waste sorting solutions
• OvA and OvR classification strategies
• confidence thresholds
• human review

Claims Made by the Document

• AI-based waste sorting solutions can support residents in proper waste disposal.
• These solutions must be configurable to reflect individual municipalities' waste sorting schemes.

Evidence Used

• The text mentions AI-based waste sorting solutions as a means of assisting residents and the need for configuration based on specific municipalities.

Methods / Approach

• Evaluation and analysis of OvA and OvR classification strategies
• Applying confidence thresholds to identify uncertain samples for human review

Limitations Stated by the Authors

The authors did not discuss any limitations in their work.

Limitations Noted by the Agent

Agent interpretation — not from the source.

It's unknown if there are any limitations due to lack of information in the provided text.

Open Questions

• What is the overall performance of OvA and OvR classification strategies?
• How do varying confidence thresholds impact the performance of OvA and OvR?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text focuses on the evaluation methods and objectives, but it does not provide specific results or discussions about the overall performance of the OvA and OvR strategies.

Raw Abstract / Excerpt

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.