Shen, G.-T.; Huang, Y.-F. Dual-Pyramid Wide Residual Network for Semantic Segmentation on Cross-Style Datasets. Information2023, 14, 630.
Shen, G.-T.; Huang, Y.-F. Dual-Pyramid Wide Residual Network for Semantic Segmentation on Cross-Style Datasets. Information 2023, 14, 630.
Shen, G.-T.; Huang, Y.-F. Dual-Pyramid Wide Residual Network for Semantic Segmentation on Cross-Style Datasets. Information2023, 14, 630.
Shen, G.-T.; Huang, Y.-F. Dual-Pyramid Wide Residual Network for Semantic Segmentation on Cross-Style Datasets. Information 2023, 14, 630.
Abstract
Image segmentation is the process of partitioning an image into multiple segments where the goal is to simplify the representation of the image and make the image more meaningful and easier to analyze. In particular, semantic segmentation is an approach of detecting the classes of objects, based on each pixel. In the past, most semantic segmentation models were for only one single style, such as urban street views, medical images, or even Manga. In this paper, we proposed a semantic segmentation model called Dual-Pyramid Wide Residual Network (DPWRN) to solve the segmentation for cross-style datasets, which is suitable for diverse segmentation applications. The DPWRN integrated the Pyramid of Kernel paralleled with Dilation (PKD) and Multi-Feature Fusion (MFF) to improve the accuracy of segmentation. To evaluate the generalization of the DPWRN and its superiority over most state-of-the-art models, three datasets with completely different styles are tested in the experiments. Finally, the DPWRN verified its generalization and also showed its superiority over most state-of-the-art models.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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