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arxiv: 2607.02230 · v1 · pith:PIPORZRPnew · submitted 2026-07-02 · 💻 cs.CV · cs.AI

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

Mohammed Fahad Ali , Dominique Briechle , Marit Briechle-Mathiszig , Tobias Geger , Andreas Rausch This is my paper

Pith reviewed 2026-07-03 15:52 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords waste sortingone-vs-allone-vs-resthuman-in-the-loopconfidence thresholdcircular economymulti-class classificationGermany
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The pith

Confidence thresholds on OvA and OvR models trade off fewer waste-sorting errors against the volume of cases sent for human review.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper compares One-vs-All and One-vs-Rest classification on images of household waste sorted according to the city of Goslar's rules. It tests whether the models' confidence scores can reliably mark uncertain predictions so those items can be passed to a human annotator instead of being shown to residents. The central goal is to lower the rate of incorrect disposal advice while keeping the human workload within practical limits. If the thresholds work as intended, municipalities gain a practical way to adapt an AI app to their own waste categories without needing a perfect classifier from the start.

Core claim

By training OvA and OvR models on a dataset built to match Goslar's waste categories and then sweeping confidence thresholds, the fraction of misclassified items that reach users can be reduced while the number of samples routed to human review remains controllable.

What carries the argument

Confidence threshold applied to OvA and OvR output scores to select uncertain samples for human-in-the-loop review.

If this is right

  • Varying the threshold produces explicit accuracy-effort curves for each strategy.
  • OvA and OvR can differ in how sharply their confidence scores separate correct from incorrect predictions.
  • The same pipeline can be retrained on any other municipality's category list without changing the human-review logic.
  • The approach supports incremental deployment: start with a loose threshold and tighten it as more labeled data arrives.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The threshold method could be reused in other image-classification settings where local rules change and some human oversight is available.
  • If the correlation between confidence and error holds, the human labels collected on uncertain cases can be fed back to retrain the model without labeling the entire stream.
  • Municipalities could begin with a conservative threshold and raise it over time once resident feedback confirms the model's reliability.

Load-bearing premise

The dataset matches real Goslar waste items and rules, and lower model confidence scores actually mark items that would be misclassified.

What would settle it

Run the trained models on a fresh collection of real Goslar household waste photos with ground-truth labels and check whether the error rate below the chosen threshold is substantially higher than the error rate above it.

Figures

Figures reproduced from arXiv: 2607.02230 by Andreas Rausch, Dominique Briechle, Marit Briechle-Mathiszig, Mohammed Fahad Ali, Tobias Geger.

Figure 2
Figure 2. Figure 2: Overview of the extended categories in the waste management dataset. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: AI-based architecture for evaluating and comparing One-Vs-All (upper branch) with One-Vs-Rest (lower branch) classification strategy. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 6
Figure 6. Figure 6: Classification report for each binary classifier within the One-Vs-Rest [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 4
Figure 4. Figure 4: Classification report summarizing the performance of the One-Vs-All [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Confusion matrix representing the number of misclassifications of the [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: Overview of the number of misclassified samples within the One-Vs [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Analyzing the misclassified samples in the specified groups in the [PITH_FULL_IMAGE:figures/full_fig_p006_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Analyzing the misclassified samples in the specified groups in the [PITH_FULL_IMAGE:figures/full_fig_p007_10.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript evaluates One-vs-All (OvA) and One-vs-Rest (OvR) classification strategies on a dataset constructed to match the waste categories and sorting scheme of Goslar, Germany. It applies varying confidence thresholds to flag uncertain samples for human review in a human-in-the-loop setup, with the goal of balancing the number of misclassifications against the human annotation effort required for effective municipal waste sorting to support the circular economy.

Significance. A well-executed empirical comparison that identifies which strategy better trades off error reduction against human review cost on a municipality-specific dataset could inform practical deployment of AI tools for waste sorting. The work's focus on confidence-guided human-in-the-loop is relevant to real-world constraints, but the absence of any reported performance metrics, dataset statistics, error bars, or method details prevents assessment of whether the claimed balance is achieved.

major comments (2)
  1. The manuscript contains no empirical results, performance numbers, dataset statistics, or method details (e.g., model architectures, training procedures, or threshold selection), so it is impossible to verify whether either strategy actually achieves the stated balance between misclassifications and human effort.
  2. No validation is provided that the constructed Goslar-aligned dataset reflects real-world waste distributions or that model confidence scores reliably predict misclassifications, which is load-bearing for the central human-in-the-loop claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed review and constructive comments on our manuscript. We address each of the major comments below and outline the revisions we will make to strengthen the paper.

read point-by-point responses

  1. Referee: The manuscript contains no empirical results, performance numbers, dataset statistics, or method details (e.g., model architectures, training procedures, or threshold selection), so it is impossible to verify whether either strategy actually achieves the stated balance between misclassifications and human effort.

    Authors: We agree with this observation. The submitted manuscript focuses on describing the evaluation framework and the comparison approach but omits the specific quantitative results and implementation details. This limits the ability to assess the findings. In the revised manuscript, we will add comprehensive empirical results including performance metrics (accuracy, precision, recall for OvA and OvR), dataset statistics (number of samples per class, total size), model architectures used, training procedures, and the method for selecting confidence thresholds. We will also include figures showing the trade-off between misclassifications and human review effort at different thresholds. revision: yes

  2. Referee: No validation is provided that the constructed Goslar-aligned dataset reflects real-world waste distributions or that model confidence scores reliably predict misclassifications, which is load-bearing for the central human-in-the-loop claim.

    Authors: This is a valid point. While the dataset was constructed to align with Goslar's waste categories and sorting scheme, we did not include explicit validation against real-world distributions or calibration analysis for the confidence scores. In the revision, we will expand the dataset section to describe the construction process in more detail, provide any available statistics or sources used for alignment, and add an analysis of the relationship between model confidence and actual misclassification rates to support the human-in-the-loop strategy. We will also discuss limitations regarding the representativeness of the dataset. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical evaluation

full rationale

The paper is an empirical comparison of OvA vs OvR strategies with confidence thresholds for human-in-the-loop review on a Goslar-aligned dataset. No derivation chain, equations, fitted parameters presented as predictions, or load-bearing self-citations are described. The central claim concerns evaluation outcomes balancing misclassifications and annotation effort, which rests on experimental results rather than reducing to inputs by construction. This is a standard self-contained empirical study with no circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Only the abstract is available, so the ledger is limited to explicitly stated premises in the provided text.

axioms (1)
  • domain assumption The constructed dataset accurately reflects the waste categories and sorting scheme of the city of Goslar.
    Stated directly in the abstract as the basis for the evaluation.

pith-pipeline@v0.9.1-grok · 5773 in / 1160 out tokens · 25531 ms · 2026-07-03T15:52:04.649490+00:00 · methodology

discussion (0)

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Reference graph

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