ELUNet: an efficient and lightweight U-shape network for real-time semantic segmentation

Ai, Yufeng; Guo, Jichang; Wang, Yudong

doi:10.1117/1.jei.31.2.023019

Cited by 4 publications

(4 citation statements)

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“…In order to evaluate the superiority of DTA-UNet and other methods, it was compared with eight state-of-art or classic methods. The eight methods are U-Net 1 , UNet++ 4 , CE-Net 11 , Attention U-Net 12 , Inf-Net 24 , SmaAt-UNet 26 , ELU-Net 27 and CMUNeXt 28 . As shown in Table 2 : Compared with the above eight methods, DTA-UNet shows the best results on the four segmentation indicators.…”

Section: Methodsmentioning

confidence: 99%

UNet based on dynamic convolution decomposition and triplet attention

Li,

Yan,

Hou

et al. 2024

Sci Rep

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The robustness and generalization of medical image segmentation models are being challenged by the differences between different disease types, different image types, and different cases.Deep learning based semantic segmentation methods have been providing state-of-the-art performance in the last few years. One deep learning technique, U-Net, has become the most popular architecture in the medical imaging segmentation. Despite outstanding overall performance in segmenting medical images, it still has the problems of limited feature expression ability and inaccurate segmentation. To this end, we propose a DTA-UNet based on Dynamic Convolution Decomposition (DCD) and Triple Attention (TA). Firstly, the model with Attention U-Net as the baseline network uses DCD to replace all the conventional convolution in the encoding-decoding process to enhance its feature extraction capability. Secondly, we combine TA with Attention Gate (AG) to be used for skip connection in order to highlight lesion regions by removing redundant information in both spatial and channel dimensions. The proposed model are tested on the two public datasets and actual clinical dataset such as the public COVID-SemiSeg dataset, the ISIC 2018 dataset, and the cooperative hospital stroke segmentation dataset. Ablation experiments on the clinical stroke segmentation dataset show the effectiveness of DCD and TA with only a 0.7628 M increase in the number of parameters compared to the baseline model. The proposed DTA-UNet is further evaluated on the three datasets of different types of images to verify its universality. Extensive experimental results show superior performance on different segmentation metrics compared to eight state-of-art methods.The GitHub URL of our code is https://github.com/shuaihou1234/DTA-UNet.

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Section: Methodsmentioning

confidence: 99%

UNet based on dynamic convolution decomposition and triplet attention

Li,

Yan,

Hou

et al. 2024

Sci Rep

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“…In recent years, there has been an increasing demand for fast response and high-quality segmentation in various applications. As a result, more and more researchers have focused on real-time semantic segmentation, 15 , 17 – 21 aiming to achieve more accurate segmentation results at faster inference speeds (e.g., more than 30 FPS). To boost the inference speed, methods such as efficient neural network (ENet), 15 CGNet, 17 and DABNet 18 have been proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Fast attention network (FANet) 30 designs a fast attention for features of each stage to enhance contextual information with a minimal computational cost. Efficient and lightweight U-shape network (ELUNet) 21 proposes a bridge channel attention module to emphasize useful information and suppress irrelevant information in multi-scale features. Multi-scale fusion network (MsNet) 31 designs two types of channel attentions to match different features, including a matching attention with low computation for low-level features and a matching attention with strong global context for high-level features.…”

Section: Related Workmentioning

confidence: 99%

PBSNet: pseudo bilateral segmentation network for real-time semantic segmentation

Luo,

Liu,

Mahmoodi

2023

J. Electron. Imag.

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Achieving real-time performance while maintaining high accuracy in semantic segmentation can be a challenging task. Many existing methods adopt multi-branch architectures to extract both spatial and semantic information, resulting in increased computational complexity and a lack of communication between branches. We propose a pseudo bilateral segmentation network (PBSNet) that can extract rich spatial and semantic features from a single path, without incurring additional computational cost or time consumption. Our proposed PBSNet utilizes a semantic enhancement module to explore the relationship between high-level semantic features, an interchange module to enhance feature representation through bi-directional vertical propagation and adaptive spatial attention, and an attention fusion module to aggregate multi-scale features to produce the final segmentation prediction. Our results on the Cityscapes dataset demonstrate the superiority of PBSNet over state-of-the-art methods, achieving a balance of accuracy and efficiency with 74.52% mean intersection over union and 82.5 frames per second.

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“…In recent years, semantic segmentation has become an important processing method for unmanned aerospace vehicle (UAV) aerial images 1 . Semantic segmentation divides an image into several visually meaningful regions and assigns them corresponding class labels 2 for the next image analysis tasks 3 . Due to the development of convolutional neural networks (CNNs), deep-learning-based semantic segmentation techniques have significantly progressed.…”

Section: Introductionmentioning

confidence: 99%

Boundary refinement network with semantic embedding connections for UAV aerial image semantic segmentation

Li,

Shi,

Kong

et al. 2023

J. Electron. Imag.

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Deep-learning-based semantic segmentation is the research focus for unmanned aerospace vehicle (UAV) aerial images analysis. However, there are problems in segmenting small and narrow objects and boundary regions, due to the large size differences between objects and the unbalanced class data in aerial images. A network named SEC-BRNet is proposed for the boundary refinement problem. First, the semantic embedding connections and progressive upsampling decoder are used to obtain spatial details for generating fused feature maps, which are then concatenated in decoding process level by level for recovering the boundary details. Second, a multiloss training strategy is developed for data imbalance and boundary roughness problems, including cross-entropy loss, Dice loss, and active boundary loss. After extensive experiments, our network could achieve 84.8% mIoU and 89.04% Boundary IoU on the AeroScapes dataset and achieve 62.81% mIoU and 90.78% Boundary IoU on the Semantic Drone Dataset. The experimental results indicate that the proposed SEC-BRNet performs well in semantic segmentation task for UAV aerial images.

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ELUNet: an efficient and lightweight U-shape network for real-time semantic segmentation

Cited by 4 publications

References 0 publications

UNet based on dynamic convolution decomposition and triplet attention

UNet based on dynamic convolution decomposition and triplet attention

PBSNet: pseudo bilateral segmentation network for real-time semantic segmentation

Boundary refinement network with semantic embedding connections for UAV aerial image semantic segmentation

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