Wide deep residual networks in networks

Alaeddine, Hmidi; Malek, Jihene

doi:10.1007/s11042-022-13696-0

Cited by 3 publications

(1 citation statement)

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“…The structure of the residual network is derived from the residual function, facilitat ing the deep network's acquisition of identity mapping for shallow features through re siduals [51,53,54]. This effectively addresses the issues of gradient vanishing and mode degradation caused by the continuous stacking of network depth.…”

Section: Residual Network Structurementioning

confidence: 99%

GEA-MSNet: A Novel Model for Segmenting Remote Sensing Images of Lakes Based on the Global Efficient Attention Module and Multi-Scale Feature Extraction

Li,

Weng,

Zheng

et al. 2024

Applied Sciences

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The decrease in lake area has garnered significant attention within the global ecological community, prompting extensive research in remote sensing and computer vision to accurately segment lake areas from satellite images. However, existing image segmentation models suffer from poor generalization performance, the imprecise depiction of water body edges, and the inadequate inclusion of water body segmentation information. To address these limitations and improve the accuracy of water body segmentation in remote sensing images, we propose a novel GEA-MSNet segmentation model. Our model incorporates a global efficient attention module (GEA) and multi-scale feature fusion to enhance the precision of water body delineation. By emphasizing global semantic information, our GEA-MSNet effectively learns image features from remote sensing data, enabling the accurate detection and segmentation of water bodies. This study makes three key contributions: firstly, we introduce the GEA module within the encode framework to aggregate shallow feature semantics for the improved classification accuracy of lake pixels; secondly, we employ a multi-scale feature fusion structure during decoding to expand the acceptance domain for feature extraction while prioritizing water body features in images; thirdly, extensive experiments are conducted on both scene classification datasets and Tibetan Plateau lake datasets with ablation experiments validating the effectiveness of our proposed GEA module and multi-scale feature fusion structure. Ultimately, our GEA-MSNet model demonstrates exceptional performance across multiple datasets with an average intersection ratio union (mIoU) improved to 75.49%, recall enhanced to 83.79%, pixel accuracy (PA) reaching 90.21%, and the f1-score significantly elevated to 83.25%.

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Section: Residual Network Structurementioning

confidence: 99%