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- W4380030926 abstract "Aiming at the problem of large amount of computation and complex decoding structure of encoder-decoder semantic segmentation network, an efficient binary semantic segmentation model EBSNet was proposed. Firstly, the model removed the complex decoding structure and skip connections in the mainstream encoder-decoder segmentation network, and used convolutional remodeling upsampling to directly recombine the final encoded features to generate segmentation masks, which simplified the parameter structure of the model. Secondly, the ECASA attention mechanism and Poly1CE loss are combined to further enhance the model coding ability and improve the final accuracy of the model. Segmentation experiments on DeepGlobe and CrackFores datasets show that the segmentation accuracy of the proposed model has reached the effect of surpassing the existing mainstream segmentation networks, and the segmentation speed has also been improved by multiple times." @default.
- W4380030926 created "2023-06-10" @default.
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- W4380030926 date "2023-02-01" @default.
- W4380030926 modified "2023-10-14" @default.
- W4380030926 title "EBSNet: Efficient Binary Semantic Segmentation Network" @default.
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- W4380030926 doi "https://doi.org/10.1109/acctcs58815.2023.00033" @default.
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