Pooling Attention-based Encoder–Decoder Network for semantic segmentation

Xu, Haixia; Huang, Yunjia; Hancock, Edwin R.; Wang, Shuailong; Xuan, Qijun; Zhou, Wei

doi:10.1016/j.compeleceng.2021.107260

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…The encoding and decoding network structure is commonly employed in computer vision tasks such as object detection and semantic segmentation [39,40]. The encoder acquires semantic features by reducing image resolution and noise through the downsampling process, while the decoder restores image resolution through upsampling operations.…”

Section: Encoder-decoder Architecturementioning

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: Encoder-decoder Architecturementioning

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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“…Deep convolutional networks have been the main solution for SegNet since fully convolutional network (FCN) 1 was used to implement SegNet using convolutional layers to replace the fully connected layer of Visual Geometry Group. 2 Networks such as U-Net, 3 SegNet, 4 and pooling attention-based encoder-decoder network (PAEDN) 5 utilize encoder-decoder structures to make full use of shallow features. Peng and Ma 6 proposed a structure called stride spatial pyramid pooling to capture multiscale semantic information from the high-level feature map.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale dense and attention mechanism for image semantic segmentation based on improved DeepLabv3+

et al. 2022

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. Semantic scene segmentation has become an important application in computer vision and is an essential part of intelligent transportation systems for complete scene understanding of the surrounding environment. Several methods based on convolutional neural networks have emerged, but they have some problems, including small-scale target loss, inaccurate detailed region segmentation, and boundary category confusion. Using shallow features, we exploit the capabilities of global context information according to the theory of pyramids. A weighted pyramid feature fusion module is constructed to fuse the feature maps of different scales generated by the backbone network, and the proportion of feature fusion is dynamically updated by trainable parameters. After that, a self-attention mechanism is introduced to discover information about spatial channel interdependencies. Finally, the atrous spatial pyramid pooling module of the DeepLabv3+ network is improved by connecting the atrous convolution with different dilation rates at the receptive field. The experimental results show 4.1% mean pixel accuracy and 3.92% mean intersection over union improvements in the proposed method compared with the DeepLabv3+, and the result of semantic segmentation is more accurate.

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Lightweight convolutional neural network driven by small data for asphalt pavement crack segmentation

Liang,

Zhang,

2024

Automation in Construction

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Pooling Attention-based Encoder–Decoder Network for semantic segmentation

Cited by 9 publications

References 21 publications

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

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

Multi-scale dense and attention mechanism for image semantic segmentation based on improved DeepLabv3+

Lightweight convolutional neural network driven by small data for asphalt pavement crack segmentation

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