دورية أكاديمية
Ancient mural segmentation based on multiscale feature fusion and dual attention enhancement
| العنوان: | Ancient mural segmentation based on multiscale feature fusion and dual attention enhancement |
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| المؤلفون: | Jianfang Cao, Zhen Cao, Zhiqiang Chen, Fang Wang, Xianhui Wang, Zhuolin Yang |
| المصدر: | Heritage Science, Vol 12, Iss 1, Pp 1-15 (2024) |
| بيانات النشر: | SpringerOpen, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Fine Arts LCC:Analytical chemistry |
| مصطلحات موضوعية: | Mural image segmentation, Attention mechanism, Atrous spatial pyramid pooling, Residual connection, Multiscale feature fusion, Fine Arts, Analytical chemistry, QD71-142 |
| الوصف: | Abstract To address the fuzzy segmentation boundaries, missing details, small target losses and low efficiency of traditional segmentation methods in ancient mural image segmentation scenarios, this paper proposes a mural segmentation model based on multiscale feature fusion and a dual attention-augmented segmentation model (MFAM). The model uses the MobileViT network, which integrates a coordinate attention mechanism, as the feature extraction backbone network. It attains global and local expression capabilities through self-attention, class convolution, and coordinate attention and focuses on location information to expand the receptive field and achieve improved feature extraction efficiency. An A_R_ASPP feature enhancement module is proposed for the attention-optimized residual atrous spatial pyramid pooling module. The module uses residual connections to solve the small target loss problem in murals caused by the excessive sampling rate of atrous convolution and uses a feature attention mechanism to adaptively adjust the feature map weight according to the channel importance levels. A dual attention-enhanced feature fusion module is proposed for multiscale decoder feature fusion to improve the mural segmentation effect. This module uses a cross-level aggregation strategy and an attention mechanism to weight the importance of different feature levels to obtain multilevel semantic feature representations. The model improves the mean intersection over union (MIoU) by 3.06% and the MPA by 1.81% on a mural dataset compared with other models. The model is proven to be effective at improving the segmentation details, efficiency and small target segmentation results produced for mural images, and a new method is proposed for segmenting ancient mural images. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2050-7445 |
| Relation: | https://doaj.org/toc/2050-7445 |
| DOI: | 10.1186/s40494-024-01172-x |
| URL الوصول: | https://doaj.org/article/bd67cb6cdf0947409c1deae1d14a1398 |
| رقم الأكسشن: | edsdoj.bd67cb6cdf0947409c1deae1d14a1398 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 20507445 |
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| DOI: | 10.1186/s40494-024-01172-x |