NUNI - Waste: Novel Semi-supervised Semantic Segmentation Waste Classification with Non-uniform Data Augmentation

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aut.relation.journalMultimedia Tools and Applications
dc.contributor.authorQi, Jianchun
dc.contributor.authorNguyen, Minh
dc.contributor.authorYan, Wei Qi
dc.date.accessioned2024-01-30T03:26:09Z
dc.date.available2024-01-30T03:26:09Z
dc.date.issued2024-01-25
dc.description.abstractWaste categorization and recycling are critical approaches for converting waste into valuable and functional materials, thereby significantly aiding in land preservation, reducing pollution, and optimizing resource usages. However, real-world classification and identification of recyclable waste face substantial hurdles due to the intricate and unpredictable nature of wastes, as well as the limited availability of comprehensive waste datasets. These factors limit efficacy of the existing research work in the domain of waste management. In this paper, we utilize semantic segmentation at individual pixel level and introduce a semi-supervised metod for authentic waste classification scenarios, leveraging the Zerowaste dataset. We devise a non-standard data augmentation strategy that mimics the ever-changing conditions of real-world waste environments. Additionally, we introduce an adaptive weighted loss function and dynamically adjust the ratio of positive to negative samples through a masking method, ensuring the model learns from relevant samples. Lastly, to maintain consistency between predictions made on data-augmented images and the original counterparts, we remove input perturbations. Our method proves to be effective, as verified by an array of standard experiments and ablation studies, achieved an accuracy improvement of 3.74% over the baseline Zerowaste method.
dc.identifier.citationMultimedia Tools and Applications, ISSN: 1573-7721 (Online), Springer Science and Business Media LLC. doi: 10.1007/s11042-024-18265-1
dc.identifier.doi10.1007/s11042-024-18265-1
dc.identifier.issn1573-7721
dc.identifier.urihttp://hdl.handle.net/10292/17160
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.relation.urihttps://link.springer.com/article/10.1007/s11042-024-18265-1
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject0801 Artificial Intelligence and Image Processing
dc.subject0803 Computer Software
dc.subject0805 Distributed Computing
dc.subject0806 Information Systems
dc.subjectArtificial Intelligence & Image Processing
dc.subjectSoftware Engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.subject4603 Computer vision and multimedia computation
dc.subject4605 Data management and data science
dc.subject4606 Distributed computing and systems software
dc.titleNUNI - Waste: Novel Semi-supervised Semantic Segmentation Waste Classification with Non-uniform Data Augmentation
dc.typeJournal Article
pubs.elements-id536083
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