Academic Journal
MultiDecNet: An Ensemble-Based Semantic Segmentation Architecture for Urban Scene Understanding.
| Τίτλος: | MultiDecNet: An Ensemble-Based Semantic Segmentation Architecture for Urban Scene Understanding. |
|---|---|
| Συγγραφείς: | Emek Soylu, Büşra, Güzel, Mehmet Serdar |
| Πηγή: | Information; Jun2026, Vol. 17 Issue 6, p540, 23p |
| Θεματικοί όροι: | Image segmentation, Convolutional neural networks, Artificial neural networks, Autonomous vehicles, Landscapes |
| Περίληψη: | Semantic segmentation is a fundamental task in computer vision that aims to assign a categorical label to each pixel in an image, facilitating dense and detailed scene understanding. This pixel-level classification is especially crucial in autonomous driving, where accurate environmental perception is vital for dependable object detection and safe decision-making. In this study, we propose MultiDecNet, a novel multi-decoder semantic segmentation framework designed to capture both macroscopic scene layouts and fine-grained spatial boundaries in complex urban environments. Drawing inspiration from classical networks, MultiDecNet incorporates a parallel dual-branch decoding strategy that simultaneously leverages the multi-scale context modeling of the Pyramid Pooling Module (PPM) and the structural refinement capabilities of Atrous Spatial Pyramid Pooling (ASPP). To explore the impact of modern backbone representations, we structurally modernize the feature extraction pipeline by introducing the contemporary ConvNeXt convolutional architecture as an alternative to traditional ResNet101 backbones. We extensively evaluate and compare the baseline configurations alongside our proposed MultiDecNet using both ResNet101 and ConvNeXt-Large backbones on the benchmark Cityscapes dataset. The quantitative assessments demonstrate that the MultiDecNet architecture consistently provides highly competitive performance within the scope of this comparative study, with the MultiDecNet-ConvNeXt variant achieving favorable overall scores among the evaluated methods. Furthermore, a granular, class-wise IoU and training dynamics analysis reveals that while traditional networks retain competitive boundaries for localized minority targets, the modern ConvNeXt backbone ensures faster convergence stability and balanced contextual mastery over large-scale driving layouts. Ultimately, these findings offer critical insights into architectural synergy and backbone selection, presenting a robust, scalable, and well-balanced solution for advanced autonomous navigation systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Information is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: MultiDecNet: An Ensemble-Based Semantic Segmentation Architecture for Urban Scene Understanding. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Emek+Soylu%2C+Büşra%22">Emek Soylu, Büşra</searchLink><br /><searchLink fieldCode="AR" term="%22Güzel%2C+Mehmet+Serdar%22">Güzel, Mehmet Serdar</searchLink> – Name: TitleSource Label: Source Group: Src Data: Information; Jun2026, Vol. 17 Issue 6, p540, 23p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Landscapes%22">Landscapes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Semantic segmentation is a fundamental task in computer vision that aims to assign a categorical label to each pixel in an image, facilitating dense and detailed scene understanding. This pixel-level classification is especially crucial in autonomous driving, where accurate environmental perception is vital for dependable object detection and safe decision-making. In this study, we propose MultiDecNet, a novel multi-decoder semantic segmentation framework designed to capture both macroscopic scene layouts and fine-grained spatial boundaries in complex urban environments. Drawing inspiration from classical networks, MultiDecNet incorporates a parallel dual-branch decoding strategy that simultaneously leverages the multi-scale context modeling of the Pyramid Pooling Module (PPM) and the structural refinement capabilities of Atrous Spatial Pyramid Pooling (ASPP). To explore the impact of modern backbone representations, we structurally modernize the feature extraction pipeline by introducing the contemporary ConvNeXt convolutional architecture as an alternative to traditional ResNet101 backbones. We extensively evaluate and compare the baseline configurations alongside our proposed MultiDecNet using both ResNet101 and ConvNeXt-Large backbones on the benchmark Cityscapes dataset. The quantitative assessments demonstrate that the MultiDecNet architecture consistently provides highly competitive performance within the scope of this comparative study, with the MultiDecNet-ConvNeXt variant achieving favorable overall scores among the evaluated methods. Furthermore, a granular, class-wise IoU and training dynamics analysis reveals that while traditional networks retain competitive boundaries for localized minority targets, the modern ConvNeXt backbone ensures faster convergence stability and balanced contextual mastery over large-scale driving layouts. Ultimately, these findings offer critical insights into architectural synergy and backbone selection, presenting a robust, scalable, and well-balanced solution for advanced autonomous navigation systems. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Information is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/info17060540 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 540 Subjects: – SubjectFull: Image segmentation Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Autonomous vehicles Type: general – SubjectFull: Landscapes Type: general Titles: – TitleFull: MultiDecNet: An Ensemble-Based Semantic Segmentation Architecture for Urban Scene Understanding. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Emek Soylu, Büşra – PersonEntity: Name: NameFull: Güzel, Mehmet Serdar IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20782489 Numbering: – Type: volume Value: 17 – Type: issue Value: 6 Titles: – TitleFull: Information Type: main |
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