pith. sign in

arxiv: 2606.13587 · v1 · pith:GRKZPNQKnew · submitted 2026-06-11 · 💻 cs.CV

Towards Effective Waste Segmentation for Automated Waste Recycling in Cluttered Background

Mamoona Javaid , Mubashir Noman , Abdul Hannan , Shah Nawaz , Mustansar Fiaz , Sajid Ghuffar This is my paper

Pith reviewed 2026-06-27 07:02 UTC · model grok-4.3

classification 💻 cs.CV
keywords waste segmentationcluttered scenesautomated recyclingspatial-spectral networkfeature enhancement modulesemantic segmentationdeep learning
0
0 comments X

The pith

A cascaded spatial-spectral network plus auxiliary feature enhancement module segments waste objects more effectively in cluttered scenes.

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

The paper seeks to replace large backbone networks for waste segmentation with a lighter design that still works in messy real-world scenes. It processes images first in the spatial domain to capture local object structures, then in the spectral domain to gather global context, passing information forward in a cascade. An auxiliary module sharpens object boundaries and boosts blob visibility to reduce errors caused by surrounding clutter. Experiments on three public waste datasets are used to show that the resulting masks are more accurate than those from heavier competing models.

Core claim

An optimal waste segmentation network is introduced which effectively utilizes the spatial domain to capture localized structural dependencies and the spectral domain to efficiently extract global contextual relationships. This cascaded design allows the network to progressively leverage both local and global representations across complementary domains to highlight the semantic information necessary for effective segmentation of various waste objects. Furthermore, auxiliary feature enhancement module (AFEM) is introduced to enhance the target objects' boundaries and blob amplification for better segmentation in cluttered scenarios.

What carries the argument

Cascaded spatial-spectral processing combined with the auxiliary feature enhancement module (AFEM) that amplifies boundaries and object blobs.

If this is right

  • Segmentation performance improves for waste objects surrounded by background clutter without relying on oversized backbone networks.
  • Local structural cues and global contextual cues are combined progressively rather than in parallel.
  • Target boundaries become sharper and object regions are amplified before final mask prediction.
  • The method runs efficiently enough for deployment in automated waste recycling pipelines.
  • Results hold across the ZeroWaste-aug, ZeroWaste-f, and SpectralWaste collections.

Where Pith is reading between the lines

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

  • The same cascade pattern could be tested on other cluttered segmentation problems such as debris detection or food sorting.
  • If the efficiency claim holds, the network could be embedded directly on sorting-line cameras with modest hardware.
  • Removing either the spectral branch or the AFEM would provide a minimal ablation to isolate which component drives the reported gains.
  • The design invites direct measurement of end-to-end recycling throughput when the masks are fed to a robotic picker.

Load-bearing premise

The specific cascaded spatial-spectral design together with AFEM will deliver higher segmentation accuracy on cluttered waste images than large-backbone alternatives without raising computation cost or eroding fine detail.

What would settle it

A direct comparison on the ZeroWaste-aug, ZeroWaste-f, or SpectralWaste test sets in which the proposed network shows no gain in mean intersection-over-union or requires substantially more FLOPs or runtime than the strongest existing backbone method.

Figures

Figures reproduced from arXiv: 2606.13587 by Abdul Hannan, Mamoona Javaid, Mubashir Noman, Mustansar Fiaz, Sajid Ghuffar, Shah Nawaz.

Figure 1
Figure 1. Figure 1: Overall framework of the proposed effective waste segmentation network (EWSegNet) is illustrated here. The encoder consists of four stages that provide multiscale feature representations (i.e., F1, F2, F3, F4). Each stage (i) contains Ni number of EWFE layers (where i ∈ [1, 2, 3, 4]). Before each stage, a convolution layer is used to downsample the feature maps. Feature representations of stage three are f… view at source ↗
Figure 2
Figure 2. Figure 2: Efficient waste feature extraction (EWFE) layer is shown in Fig. a). Fig b) represents the spatial context module (SCM) that is used for feature excitation and weighting in spatial domain. In c), frequency context module (FCM) is illustrated that captures global contextual relationship between pixels in frequency domain. mension and passed to a 1 × 1 convolution to obtain the weighted features X′ . Mathema… view at source ↗
Figure 3
Figure 3. Figure 3: This figure demonstrates the auxiliary feature enhance￾ment module (AFEM) that has dual functions: boundaries enhance￾ment (BE) and blob amplification (BA). BE emphasizes the fine details by using difference of Gaussian filtration while BA uses pooled attention to focus on semantic regions. 3.3. Auxiliary Feature Enhancement Module Auxiliary feature enhancement module (AFEM) is the criti￾cal component of E… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison of EWSegNet with the recent waste segmentation methods FANet (Ali et al., 2024) and COSNet (Ali et al., 2025) on ZeroWaste-f. Proposed EWSegNet provides reasonably better segmentation performance as highlighted in yellow boxes [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visual comparison of EWSegNet with recent waste segmentation methods FANet (Ali et al., 2024) and COSNet (Ali et al., 2025) on Spectral Waste dataset. As highlighted in yellow boxes, proposed EWSegNet is fairly better to segment the waste objects in cluttered scenes [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Here we demonstrate the efficacy of AFEM. a) shows the input image to EWSegNet, b) is the visualization of 3rd stage features which are input to AFEM, c) shows the visualization of the output features of AFEM, d) represents the boundaries highlighted by BE part in AFEM, e) shows the blobs emphasized by BA part in AFEM, f) is prediction of the EWSegNet, and g) shows the Ground Truth segmentation map [PITH_… view at source ↗
Figure 7
Figure 7. Figure 7: illustrates a few examples from the ZeroWaste-f dataset where the proposed EWSegNet and COSNet (Ali et al., 2025) struggle to accurately detect waste objects. We notice that both methods have some false detections in the first three rows. Moreover, the models struggle to identify some cardboard objects in the last two rows [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
read the original abstract

Rapid expansion of urban areas and population growth is causing an immense increase in waste production, which demands the need for efficient and automated waste management. In this scenario, automated waste recycling (AWR) using deep learning methods can assist humans in optimal waste management. Recent deep learning approaches for AWR provide promising waste segmentation performance, however, these methods rely on large backbone networks that are inefficient for AWR systems and suffer from performance deterioration in cluttered scenes. To this end, an optimal waste segmentation network is introduced which effectively utilizes the spatial domain to capture localized structural dependencies and the spectral domain to efficiently extract global contextual relationships. This cascaded design allows the network to progressively leverage both local and global representations across complementary domains to highlight the semantic information necessary for effective segmentation of various waste objects. Furthermore, auxiliary feature enhancement module (AFEM) is introduced to enhance the target objects' boundaries and blob amplification for better segmentation in cluttered scenarios. Extensive experimentation on ZeroWaste-aug, ZeroWaste-f and SpectralWaste datasets reveals the merits of the proposed method.

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

1 major / 1 minor

Summary. The manuscript proposes a cascaded spatial-spectral waste segmentation network that leverages spatial domain for localized structural dependencies and spectral domain for global contextual relationships, supplemented by an auxiliary feature enhancement module (AFEM) to enhance boundaries and blob amplification in cluttered scenes. It claims that extensive experiments on ZeroWaste-aug, ZeroWaste-f, and SpectralWaste datasets demonstrate the merits of this approach over existing methods relying on large backbones.

Significance. If the performance claims hold with appropriate metrics and comparisons, the work could contribute to more efficient automated waste recycling systems by reducing reliance on computationally heavy backbone networks while improving segmentation in challenging cluttered environments. The dual-domain cascaded design and AFEM represent potentially useful architectural innovations for semantic segmentation tasks.

major comments (1)
  1. [Abstract] Abstract: The abstract asserts that 'extensive experimentation on ZeroWaste-aug, ZeroWaste-f and SpectralWaste datasets reveals the merits of the proposed method,' yet provides no quantitative metrics, baseline comparisons, ablation studies, error bars, or implementation details. This omission prevents evaluation of the central performance claims and the assertion of superiority in cluttered scenarios.
minor comments (1)
  1. The title and abstract use 'optimal' which may be overstated without comparative evidence; consider rephrasing to 'proposed' or 'efficient'.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed review and constructive feedback on our manuscript. We address the major comment point-by-point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract asserts that 'extensive experimentation on ZeroWaste-aug, ZeroWaste-f and SpectralWaste datasets reveals the merits of the proposed method,' yet provides no quantitative metrics, baseline comparisons, ablation studies, error bars, or implementation details. This omission prevents evaluation of the central performance claims and the assertion of superiority in cluttered scenarios.

    Authors: We agree that the abstract would benefit from including key quantitative results to better substantiate the performance claims upfront. The full manuscript provides these details in the Experiments section (including tables with mIoU, comparisons to baselines, and ablations), but the abstract itself is currently qualitative. In the revised version, we will update the abstract to include specific metrics such as mIoU gains on the three datasets and brief baseline comparisons, while keeping it concise. Implementation details and error bars remain in the main text as is conventional. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces an architectural proposal (cascaded spatial-spectral network plus AFEM) whose performance is asserted via empirical results on external datasets. No equations, parameter fittings, derivations, or self-citation chains appear in the supplied text. The central claim reduces to experimental validation rather than any self-referential reduction or renamed input, satisfying the criteria for a self-contained empirical contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no mathematical formulation, parameters, or explicit assumptions; ledger remains empty.

pith-pipeline@v0.9.1-grok · 5730 in / 1033 out tokens · 30188 ms · 2026-06-27T07:02:10.560199+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

67 extracted references · 10 canonical work pages · 1 internal anchor

  1. [1]

    and Bhada-Tata, Perinaz and Van Woerden, Frank , title=

    Kaza, Silpa and Yao, Lisa C. and Bhada-Tata, Perinaz and Van Woerden, Frank , title=. Urban Development , organization=. 2018 , url=

    2018

  2. [2]

    CVPR , pages=

    Zerowaste dataset: Towards deformable object segmentation in cluttered scenes , author=. CVPR , pages=

  3. [3]

    2016 , eprint=

    SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , author=. 2016 , eprint=

    2016

  4. [4]

    2020 , eprint=

    Segmenting Transparent Objects in the Wild , author=. 2020 , eprint=

    2020

  5. [5]

    2017 , note =

    A review on automated sorting of source-separated municipal solid waste for recycling , journal =. 2017 , note =. doi:https://doi.org/10.1016/j.wasman.2016.09.015 , url =

    work page doi:10.1016/j.wasman.2016.09.015 2017

  6. [6]

    2021 , eprint=

    Swin Transformer: Hierarchical Vision Transformer using Shifted Windows , author=. 2021 , eprint=

    2021

  7. [7]

    2021 , eprint=

    Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation , author=. 2021 , eprint=

    2021

  8. [8]

    2017 , eprint=

    Pyramid Scene Parsing Network , author=. 2017 , eprint=

    2017

  9. [9]

    2017 , eprint=

    Rethinking Atrous Convolution for Semantic Image Segmentation , author=. 2017 , eprint=

    2017

  10. [10]

    ECCV , pages=

    Encoder-decoder with atrous separable convolution for semantic image segmentation , author=. ECCV , pages=

  11. [11]

    International Conference on Learning Representations , year=

    Bootstrapping Semantic Segmentation with Regional Contrast , author=. International Conference on Learning Representations , year=

  12. [12]

    The Essential Guide to Image Processing , publisher =

    Chapter 19 - Gradient and Laplacian Edge Detection , editor =. The Essential Guide to Image Processing , publisher =. 2009 , isbn =. doi:https://doi.org/10.1016/B978-0-12-374457-9.00019-6 , url =

    work page doi:10.1016/b978-0-12-374457-9.00019-6 2009

  13. [13]

    CVPR , year=

    Scene Parsing through ADE20K Dataset , author=. CVPR , year=

  14. [14]

    CVPR , month =

    Zhao, Hengshuang and Shi, Jianping and Qi, Xiaojuan and Wang, Xiaogang and Jia, Jiaya , title =. CVPR , month =

  15. [15]

    IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

    DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , author=. IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

  16. [16]

    ECCV , pages=

    Unified perceptual parsing for scene understanding , author=. ECCV , pages=

  17. [17]

    Advances in Neural Information Processing Systems , volume=

    Focal modulation networks , author=. Advances in Neural Information Processing Systems , volume=

  18. [18]

    MMSegmentation Contributors , howpublished =

  19. [19]

    International Conference on Learning Representations , year=

    Decoupled Weight Decay Regularization , author=. International Conference on Learning Representations , year=

  20. [20]

    ImageNet: A large-scale hierarchical image database , year=

    Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Kai Li and Li Fei-Fei , booktitle=. ImageNet: A large-scale hierarchical image database , year=

  21. [21]

    Semi-Supervised Semantic Segmentation With Cross-Consistency Training , year=

    Ouali, Yassine and Hudelot, Céline and Tami, Myriam , booktitle=. Semi-Supervised Semantic Segmentation With Cross-Consistency Training , year=

  22. [22]

    CVPR , year =

    Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie , title =. CVPR , year =

  23. [23]

    ArXiv , year=

    Focal Self-attention for Local-Global Interactions in Vision Transformers , author=. ArXiv , year=

  24. [24]

    2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages=

    Spectralwaste dataset: Multimodal data for waste sorting automation , author=. 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages=. 2024 , organization=

    2024

  25. [25]

    Waste Management , volume=

    A robust framework combined saliency detection and image recognition for garbage classification , author=. Waste Management , volume=. 2022 , publisher=

    2022

  26. [26]

    Signal, Image and Video Processing , volume=

    YOLO-MTG: a lightweight YOLO model for multi-target garbage detection , author=. Signal, Image and Video Processing , volume=. 2024 , publisher=

    2024

  27. [27]

    arXiv preprint arXiv:2209.15159 , year=

    Mobilevitv3: Mobile-friendly vision transformer with simple and effective fusion of local, global and input features , author=. arXiv preprint arXiv:2209.15159 , year=

    work page arXiv

  28. [28]

    Resources, Conservation and Recycling , volume=

    Recycling waste classification using optimized convolutional neural network , author=. Resources, Conservation and Recycling , volume=. 2021 , publisher=

    2021

  29. [29]

    Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

    Densely connected convolutional networks , author=. Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

  30. [30]

    Convolutional neural networks with swift for Tensorflow: image recognition and dataset categorization , pages=

    EfficientNet , author=. Convolutional neural networks with swift for Tensorflow: image recognition and dataset categorization , pages=. 2021 , publisher=

    2021

  31. [31]

    International Journal of Environmental Research and Public Health , volume=

    An intelligent waste-sorting and recycling device based on improved EfficientNet , author=. International Journal of Environmental Research and Public Health , volume=. 2022 , publisher=

    2022

  32. [32]

    , journal=

    Alonso, Iñigo and Riazuelo, Luis and Murillo, Ana C. , journal=. MiniNet: An Efficient Semantic Segmentation ConvNet for Real-Time Robotic Applications , year=

  33. [33]

    and Luo, Ping , booktitle =

    Xie, Enze and Wang, Wenhai and Yu, Zhiding and Anandkumar, Anima and Alvarez, Jose M. and Luo, Ping , booktitle =. SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers , volume =

  34. [34]

    MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

    Mobilenets: Efficient convolutional neural networks for mobile vision applications , author=. arXiv preprint arXiv:1704.04861 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  35. [35]

    Journal of Electrical Engineering & Technology , volume=

    A mobilenet-SSD model with FPN for waste detection , author=. Journal of Electrical Engineering & Technology , volume=. 2022 , publisher=

    2022

  36. [36]

    IEEE Access , volume=

    Garbage classification algorithm based on improved mobilenetv3 , author=. IEEE Access , volume=. 2024 , publisher=

    2024

  37. [37]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Searching for mobilenetv3 , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  38. [38]

    ECCV , pages=

    Cbam: Convolutional block attention module , author=. ECCV , pages=

  39. [39]

    IEEE Access , year=

    A reliable and robust deep learning model for effective recyclable waste classification , author=. IEEE Access , year=

  40. [40]

    Multimedia Tools and Applications , pages=

    NUNI-Waste: novel semi-supervised semantic segmentation waste classification with non-uniform data augmentation , author=. Multimedia Tools and Applications , pages=. 2024 , publisher=

    2024

  41. [41]

    Fanet: Feature Amplification Network for Semantic Segmentation in Cluttered Background , year=

    Ali, Muhammad and Javaid, Mamoona and Noman, Mubashir and Fiaz, Mustansar and Khan, Salman , booktitle=. Fanet: Feature Amplification Network for Semantic Segmentation in Cluttered Background , year=

  42. [42]

    Engineering Applications of Artificial Intelligence , volume=

    Hierarchical waste detection with weakly supervised segmentation in images from recycling plants , author=. Engineering Applications of Artificial Intelligence , volume=. 2024 , publisher=

    2024

  43. [43]

    Journal of Marine Science and Engineering , volume=

    Real-time instance segmentation for detection of underwater litter as a plastic source , author=. Journal of Marine Science and Engineering , volume=. 2023 , publisher=

    2023

  44. [44]

    Proceedings of the IEEE International Conference on Computer Vision , pages=

    Mask r-cnn , author=. Proceedings of the IEEE International Conference on Computer Vision , pages=

  45. [45]

    arXiv preprint arXiv:2310.19407 , year=

    Resource Constrained Semantic Segmentation for Waste Sorting , author=. arXiv preprint arXiv:2310.19407 , year=

    work page arXiv

  46. [46]

    arXiv preprint arXiv:2401.15175 , year=

    Kitchen Food Waste Image Segmentation and Classification for Compost Nutrients Estimation , author=. arXiv preprint arXiv:2401.15175 , year=

    work page arXiv

  47. [47]

    CVPR , pages=

    Internimage: Exploring large-scale vision foundation models with deformable convolutions , author=. CVPR , pages=

  48. [48]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Scale-aware modulation meet transformer , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  49. [49]

    IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

    Conv2former: A simple transformer-style convnet for visual recognition , author=. IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

  50. [50]

    CVPR , month =

    Fan, Mingyuan and Lai, Shenqi and Huang, Junshi and Wei, Xiaoming and Chai, Zhenhua and Luo, Junfeng and Wei, Xiaolin , title =. CVPR , month =. 2021 , pages =

    2021

  51. [51]

    ECCV , pages=

    Bisenet: Bilateral segmentation network for real-time semantic segmentation , author=. ECCV , pages=. 2018 , organization=

    2018

  52. [52]

    2020 , eprint=

    The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation , author=. 2020 , eprint=

    2020

  53. [53]

    Wang and J

    W. Wang and J. Dai and Z. Chen and Z. Huang and Z. Li and X. Zhu and X. Hu and T. Lu and L. Lu and H. Li and X. Wang and Y. Qiao , booktitle =. InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions , year =

  54. [54]

    2025 , issn =

    GD-YOLO: A lightweight model for household waste image detection , journal =. 2025 , issn =. doi:https://doi.org/10.1016/j.eswa.2025.127525 , url =

    work page doi:10.1016/j.eswa.2025.127525 2025

  55. [55]

    Proceedings of the Winter Conference on Applications of Computer Vision (WACV) , month =

    Ali, Muhammad and Javaid, Mamoona and Noman, Mubashir and Fiaz, Mustansar and Khan, Salman , title =. Proceedings of the Winter Conference on Applications of Computer Vision (WACV) , month =. 2025 , pages =

    2025

  56. [56]

    and Woods, R.E

    Gonzalez, R.C. and Woods, R.E. , chapter=. Intensity Transformations and Spatial Filtering , booktitle=. 2018 , publisher=

    2018

  57. [57]

    and Woods, R.E

    Gonzalez, R.C. and Woods, R.E. , chapter=. Filtering in the Frequency Domain , booktitle=. 2018 , publisher=

    2018

  58. [58]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Scale-aware trident networks for object detection , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  59. [59]

    2025 , issn =

    Leveraging machine learning for sustainable solid waste management: A global perspective , journal =. 2025 , issn =. doi:https://doi.org/10.1016/j.sftr.2025.101098 , url =

    work page doi:10.1016/j.sftr.2025.101098 2025

  60. [60]

    2026 , issn =

    Lightweight context-awareness hybrid-attention network for waste segmentation in cluttered scenes , journal =. 2026 , issn =. doi:https://doi.org/10.1016/j.displa.2025.103213 , url =

    work page doi:10.1016/j.displa.2025.103213 2026

  61. [61]

    Taco: Trash annotations in context for litter detection,

    Taco: Trash annotations in context for litter detection , author=. arXiv preprint arXiv:2003.06975 , year=

    work page arXiv 2003

  62. [62]

    CVPR , pages=

    PIDNet: A real-time semantic segmentation network inspired by PID controllers , author=. CVPR , pages=

  63. [63]

    Proceedings of the AAAI conference on artificial intelligence , volume=

    Head-free lightweight semantic segmentation with linear transformer , author=. Proceedings of the AAAI conference on artificial intelligence , volume=

  64. [64]

    CVPR , pages=

    Topformer: Token pyramid transformer for mobile semantic segmentation , author=. CVPR , pages=

  65. [65]

    The eleventh international conference on learning representations , year=

    Seaformer: Squeeze-enhanced axial transformer for mobile semantic segmentation , author=. The eleventh international conference on learning representations , year=

  66. [66]

    Proceedings of the AAAI conference on artificial intelligence , volume=

    FeedFormer: Revisiting transformer decoder for efficient semantic segmentation , author=. Proceedings of the AAAI conference on artificial intelligence , volume=

  67. [67]

    IEEE Transactions on Intelligent Transportation Systems , volume=

    Deep dual-resolution networks for real-time and accurate semantic segmentation of traffic scenes , author=. IEEE Transactions on Intelligent Transportation Systems , volume=. 2022 , publisher=

    2022