دورية أكاديمية
MAFNet: dual-branch fusion network with multiscale atrous pyramid pooling aggregate contextual features for real-time semantic segmentation
| العنوان: | MAFNet: dual-branch fusion network with multiscale atrous pyramid pooling aggregate contextual features for real-time semantic segmentation |
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| المؤلفون: | Shan Zhao, Yunlei Wang, Xuan Wu, Fukai Zhang |
| المصدر: | Complex & Intelligent Systems, Vol 10, Iss 4, Pp 5107-5126 (2024) |
| بيانات النشر: | Springer, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Electronic computers. Computer science LCC:Information technology |
| مصطلحات موضوعية: | Semantic segmentation, Real time, Multiscale, Pyramid pooling, Autonomous driving, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64 |
| الوصف: | Abstract Currently, many real-time semantic segmentation networks aim for heightened accuracy, inevitably leading to increased computational complexity and reduced inference speed. Therefore, striking a balance between accuracy and speed has emerged as a crucial concern in this domain. To address these challenges, this study proposes a dual-branch fusion network with multiscale atrous pyramid pooling aggregate contextual features for real-time semantic segmentation (MAFNet). The first key component, the semantics guide spatial-details module (SGSDM) not only facilitates precise boundary extraction and fine-grained classification, but also provides semantic-based feature representation, thereby enhancing support for spatial analysis and decision boundaries. The second component, the multiscale atrous pyramid pooling module (MSAPPM), is designed by combining dilation convolution with feature pyramid pooling operations at various dilation rates. This design not only expands the receptive field, but also aggregates rich contextual information more effectively. To further improve the fusion of feature information generated by the dual-branch, a bilateral fusion module (BFM) is introduced. This module employs cross-fusion by calculating weights generated by the dual-branch to balance the weight relationship between the dual branches, thereby achieving effective feature information fusion. To validate the effectiveness of the proposed network, experiments are conducted on a single A100 GPU. MAFNet achieves a mean intersection over union (mIoU) of 77.4% at 70.9 FPS on the Cityscapes test dataset and 77.6% mIoU at 192.5 FPS on the CamVid test dataset. The experimental results conclusively demonstrated that MAFNet effectively strikes a balance between accuracy and speed. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2199-4536 2198-6053 |
| Relation: | https://doaj.org/toc/2199-4536; https://doaj.org/toc/2198-6053 |
| DOI: | 10.1007/s40747-024-01428-w |
| URL الوصول: | https://doaj.org/article/ff1a76ea65a1463cba554ef023f127aa |
| رقم الأكسشن: | edsdoj.ff1a76ea65a1463cba554ef023f127aa |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 21994536 21986053 |
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| DOI: | 10.1007/s40747-024-01428-w |