Enhanced boundary perception and streamlined instance segmentation
Abstract Instance segmentation is a crucial task in computer vision that aims to simultaneously identify and segment individual objects within images. While existing approaches such as Mask R-CNN have shown promise, they often struggle with accurate boundary detection, especially for complex objects...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-09139-z |
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| Summary: | Abstract Instance segmentation is a crucial task in computer vision that aims to simultaneously identify and segment individual objects within images. While existing approaches such as Mask R-CNN have shown promise, they often struggle with accurate boundary detection, especially for complex objects. In this paper, we introduce BorderMask, a novel framework that enhances boundary perception and streamlines instance segmentation. BorderMask comprises three key innovations: the Multiscale Boundary Perception Enhanced Attention (MBPEA) module, which iteratively optimizes multi-scale boundary features; the Cross-modal Link Structure (CMLS), which enables information exchange between detection and segmentation branches; and the Equilibrium Map loss function, which mitigates class imbalance issues. Extensive experiments on benchmark datasets including MS COCO, PASCAL VOC 2012, and Cityscapes demonstrate that BorderMask significantly outperforms state-of-the-art methods, achieving an AP of 44.7% on MS COCO, underscoring its robustness and effectiveness. The code will be available at https://gitee.com/shi-junyong/BorderMask . |
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| ISSN: | 2045-2322 |