ESNet: An Efficient Symmetric Network for Real-Time Semantic Segmentation

Wang, Yu; Zhou, Quan; Xiong, Jian; Wu, Xiaofu; Jin, Xin

doi:10.1007/978-3-030-31723-2_4

Cited by 55 publications

(31 citation statements)

References 35 publications

(127 reference statements)

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“…To segment the COVID-19 infection from lung CT images, LungINFseg is compared with the state-of-the-art segmentation models, such as FCN [ 15 ], UNet [ 16 ], SegNet [ 17 ], FSSNet [ 18 ], SQNet [ 19 ], ContextNet [ 20 ], EDANet [ 21 ], CGNet [ 22 ], ERFNet [ 23 ], ESNet [ 24 ], DABNet [ 25 ], Inf-Net [ 12 ], and MIScnn [ 26 ] models. All these models are assessed both quantitatively and qualitatively.…”

Section: Resultsmentioning

confidence: 99%

“… We propose the RFA module that can enlarge the receptive field of the segmentation models and increase the learning ability of the model without information loss. We present a comprehensive comparison with 13 state-of-the-art segmentation models, namely, FCN [ 15 ], UNet [ 16 ], SegNet [ 17 ], FSSNet [ 18 ], SQNet [ 19 ], ContextNet [ 20 ], EDANet [ 21 ], CGNet [ 22 ], ERFNet [ 23 ], ESNet [ 24 ], DABNet [ 25 ], Inf-Net [ 12 ], and MIScnn [ 26 ]. Extensive experiments were performed to provide ablation studies that add a thorough analysis of the proposed LungINFSeg (e.g., the effect of resolution size and variation of the loss function).…”

Section: Introductionmentioning

confidence: 99%

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LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework

et al. 2021

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COVID-19 is a fast-growing disease all over the world, but facilities in the hospitals are restricted. Due to unavailability of an appropriate vaccine or medicine, early identification of patients suspected to have COVID-19 plays an important role in limiting the extent of disease. Lung computed tomography (CT) imaging is an alternative to the RT-PCR test for diagnosing COVID-19. Manual segmentation of lung CT images is time consuming and has several challenges, such as the high disparities in texture, size, and location of infections. Patchy ground-glass and consolidations, along with pathological changes, limit the accuracy of the existing deep learning-based CT slices segmentation methods. To cope with these issues, in this paper we propose a fully automated and efficient deep learning-based method, called LungINFseg, to segment the COVID-19 infections in lung CT images. Specifically, we propose the receptive-field-aware (RFA) module that can enlarge the receptive field of the segmentation models and increase the learning ability of the model without information loss. RFA includes convolution layers to extract COVID-19 features, dilated convolution consolidated with learnable parallel-group convolution to enlarge the receptive field, frequency domain features obtained by discrete wavelet transform, which also enlarges the receptive field, and an attention mechanism to promote COVID-19-related features. Large receptive fields could help deep learning models to learn contextual information and COVID-19 infection-related features that yield accurate segmentation results. In our experiments, we used a total of 1800+ annotated CT slices to build and test LungINFseg. We also compared LungINFseg with 13 state-of-the-art deep learning-based segmentation methods to demonstrate its effectiveness. LungINFseg achieved a dice score of 80.34% and an intersection-over-union (IoU) score of 68.77%—higher than the ones of the other 13 segmentation methods. Specifically, the dice and IoU scores of LungINFseg were 10% better than those of the popular biomedical segmentation method U-Net.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework

et al. 2021

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“…However, a thorough analysis, as shown in Figure 6, explains that there are actually two human silhouettes in difficult lighting conditions, so it was even a challenge to the manual labeling process for tag assignments in Ground Truth. 5 presents the segmentation results obtained with models ESNet [22], PSP-Net [23], LEDNet [24], ContextNet [16] and our models, respectively applied to the same images, achieving a better qualitative comparison of the results. Row (a)-The original images to use and Row (b)-The ground truth.…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…However, it still presents some obvious errors in the extremities in all models, particularly the arm of some of the silhouettes framed in a yellow rectangle in our model. Finally, the segmentation results for the original image of the third column are presented: the third column row (c) column shows the segmentation results obtained using the ESNet [22] model; the third column row (d)-The segmentation results using the LEDNet model [24] and the third column row (e)-The results obtained with our model. It can be observed that in general terms, the application of a model specifically trained to detect people performs a finer detection of human silhouettes than its counterpart that uses multiple classes for training.…”

Section: Evaluation Methodsmentioning

confidence: 99%

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FASSD-Net Model for Person Semantic Segmentation

et al. 2021

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This paper proposes the use of the FASSD-Net model for semantic segmentation of human silhouettes, these silhouettes can later be used in various applications that require specific characteristics of human interaction observed in video sequences for the understanding of human activities or for human identification. These applications are classified as high-level task semantic understanding. Since semantic segmentation is presented as one solution for human silhouette extraction, it is concluded that convolutional neural networks (CNN) have a clear advantage over traditional methods for computer vision, based on their ability to learn the representations of appropriate characteristics for the task of segmentation. In this work, the FASSD-Net model is used as a novel proposal that promises real-time segmentation in high-resolution images exceeding 20 FPS. To evaluate the proposed scheme, we use the Cityscapes database, which consists of sundry scenarios that represent human interaction with its environment (these scenarios show the semantic segmentation of people, difficult to solve, that favors the evaluation of our proposal), To adapt the FASSD-Net model to human silhouette semantic segmentation, the indexes of the 19 classes traditionally proposed for Cityscapes were modified, leaving only two labels: One for the class of interest labeled as person and one for the background. The Cityscapes database includes the category “human” composed for “rider” and “person” classes, in which the rider class contains incomplete human silhouettes due to self-occlusions for the activity or transport used. For this reason, we only train the model using the person class rather than human category. The implementation of the FASSD-Net model with only two classes shows promising results in both a qualitative and quantitative manner for the segmentation of human silhouettes.

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MIFNet: A lightweight multiscale information fusion network

Cheng

Peng

Tang

et al. 2021

Int J of Intelligent Sys

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Semantic segmentation technique plays a crucial role in Internet of Things applications, such as industrial robotics and self‐driving. Recently deep learning approaches have boosted semantic segmentation accuracy greatly. However, their comprehensive performance in terms of accuracy and efficiency is still far from satisfactory. We observe that (1) accuracy‐oriented methods rely on numerous convolution layers and sophisticated architectures, which result in heavy computational complexity and usually take a long time for inference; (2) efficiency‐oriented methods fail to capture the multiscale context information for discriminative representations during the feature fusion process, thus leading to suboptimal performance. Previous semantic segmentation approaches fail to address these two challenges simultaneously. To tackle the dilemma of precise segmentation and efficient inference, we propose a novel lightweight Multiscale Information Fusion Network (MIFNet). Specifically, the proposed MIFNet mainly consists of two core components, that is, Pyramid Refinement Connection Module (PRCM) and Lightweight Information Fusion Module (LIFM). The PRCM exploits skip learning to establish dependency between different stages. Meanwhile, the pyramid attention mechanism (PAM) in PRCM, which adjusts the weight of hybrid pyramid attention vector to refine spatial features of low‐level, is developed to alleviate the semantic gap. Moreover, the LIFM is designed to detect objects at multiple scales from the global‐local perspective. In LIFM, the proposed multiscale dense concatenation (MDC) adopts various dilated convolution to extract multiscale local context information. Extensive experimental results on benchmarks data sets demonstrate the significantly better performance of the proposed MIFNet compared with most existing state‐of‐the‐art methods.

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ESNet: An Efficient Symmetric Network for Real-Time Semantic Segmentation

Cited by 55 publications

References 35 publications

LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework

LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework

FASSD-Net Model for Person Semantic Segmentation

MIFNet: A lightweight multiscale information fusion network

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