Source note3 min read
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
arXiv
arxiv.org/abs/2607.02230v1
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
This research evaluates and compares two waste sorting classification strategies, One-Vs-All (OvA) and One-Vs-Rest (OvR), for automated waste sorting in the city of Goslar, Germany. The study considers the performance of these strategies under varying confidence thresholds to identify uncertain samples that require human review.
What the Document Says
- The complexity of waste disposal regulations across European countries poses challenges for residents and hinders the transition to a Circular Economy.
- In Germany, proper sorting and disposal of household waste remains challenging across municipalities.
- 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 like mobile applications that guide proper waste disposal.
- These solutions must be configurable to reflect the specific waste sorting scheme of individual municipalities in Germany.
- In 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.
- The study aims to extend beyond 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.
Key Concepts Mentioned
- waste disposal
- Circular Economy
- Germany
- household waste
- AI-based waste sorting
- mobile applications
- One-Vs-All (OvA)
- One-Vs-Rest (OvR)
- dataset
- confidence thresholds
- human review
Claims Made by the Document
- AI-based waste sorting solutions can support residents.
- These solutions must be configurable to reflect the specific waste sorting scheme of individual municipalities in Germany.
- OvA and OvR classification strategies can identify samples likely to be misclassified.
Evidence Used
- The complexity of waste disposal regulations across European countries poses challenges for residents (implies need for AI-based solutions)
- Substantially reducing incorrectly disposed waste is vital (implies need for accurate sorting solutions)
- These solutions must be configurable (mentioned in the context of municipalities' specific waste sorting schemes)
Methods / Approach
UNKNOWN
Limitations Stated by the Authors
UNKNOWN
Limitations Noted by the Agent
Agent interpretation — not from the source.
UNKNOWN
Open Questions
UNKNOWN
Notes for Future Q&A
May include interpretation. Labeled where it does.
UNKNOWN
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.
