International Journal of Innovative Research in Engineering and Management
Year: 2025, Volume: 13, Issue: 2
First page : ( 79) Last page : ( 88)
Online ISSN : 2350-0557.
DOI: 10.55524/ijircst.2025.13.2.12 |
DOI URL: https://doi.org/10.55524/ijircst.2025.13.2.12
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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
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Svetlana Orlova , Mikhail Tarasov, Anastasia Belova, Alexey Frolov, Tatiana Zykova, Viktor Melnikov, Krzysztof Zalewski
Accurate image segmentation remains a cornerstone challenge in computer vision, particularly under open-set conditions where object variability and scene complexity hinder generalization. To address these limitations, we propose a novel visual-based methodology entitled Visual-Based Space Debris Segmentation Using an Enhanced Segment Anything Framework. This approach synergistically integrates an optimized clause-aware prompt mechanism derived from Grounding DINO with a structurally refined version of the Segment Anything Model (SAM). By embedding hierarchical non-maximum suppression and adaptive region purification through connected component filtration, we substantially augment segmentation fidelity. Furthermore, we incorporate ViT-Matte, a vision transformer-based trimap enhancement module, to improve boundary localization and reduce aliasing in edge delineation. Extensive validation on the COCO2017 benchmark reveals that our framework elevates Mean Pixel Accuracy by 6.04%, culminating at 24.74%, thereby substantiating its efficacy in foreground-background discrimination under visually ambiguous scenarios such as orbital debris fields.
Department of Aerospace Informatics, Moscow State Technical University of Civil Aviation, Russia
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