Image semantic segmentation method based on GAN network and ENet model

Li, Huiyi

doi:10.1049/tje2.12067

Cited by 11 publications

(3 citation statements)

References 30 publications

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“…Since its introduction in 2014, GAN and its variants have achieved notable success in generative image modeling and demonstrated exceptional performance in semantic segmentation [33][34][35][36]. Adversarial learning has also been shown to be suitable for smalldata training [37][38][39].…”

Section: Basic Gan and Its Variantsmentioning

confidence: 99%

Stripe Extraction of Oceanic Internal Waves Using PCGAN with Small-Data Training

Duan,

Barintag,

Meng

et al. 2024

Remote Sensing

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Playing a crucial role in ocean activities, internal solitary waves (ISWs) are of significant importance. Currently, the use of deep learning for detecting ISWs in synthetic aperture radar (SAR) imagery is gaining growing attention. However, these approaches often demand a considerable number of labeled images, which can be challenging to acquire in practice. In this study, we propose an innovative method employing a pyramidal conditional generative adversarial network (PCGAN). At each scale, it employs the framework of a conditional generative adversarial network (CGAN), comprising a generator and a discriminator. The generator works to produce internal wave patterns as authentically as possible, while the discriminator is designed to differentiate between images generated by the generator and reference images. The architecture based on pyramids adeptly captures the encompassing as well as localized characteristics of internal waves. The incorporation of upsampling further bolsters the model’s ability to recognize fine-scale internal wave stripes. These attributes endow the PCGAN with the capacity to learn from a limited amount of internal wave observation data. Experimental results affirm that the PCGAN, trained with just four internal wave images, can accurately detect internal wave stripes in the test set. Through comparative experiments with other segmentation models, we demonstrate the effectiveness and robustness of PCGAN.

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Section: Basic Gan and Its Variantsmentioning

confidence: 99%

Stripe Extraction of Oceanic Internal Waves Using PCGAN with Small-Data Training

Duan,

Barintag,

Meng

et al. 2024

Remote Sensing

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“…Although some other advanced and computationally heavy models have been proposed in recent years, the community is still carrying out meaningful experiments using the models that we have proposed here. In the future, we would explore such advanced models including DenseNet [37], Squeeze Net [38], ENet [39] besides also some vision transformers [40] and compare their performances. We have used 1D CNN on the pre-processed textual data as CNN performed the best on the texts when compared with the performance of Recurrent Neural Networks (RNNs) Our network architectures with 1D CNN on the text and 3D CNN, 3D VGG network, and 3D ResNet on vision-dose are illustrated in Figure 3.…”

Section: Deep Neural Network Architecturesmentioning

confidence: 99%

Multimodal Deep Learning Methods on Image and Textual Data to Predict Radiotherapy Structure Names

et al. 2023

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Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and ‘Other’ organs is a vital problem. This paper presents novel deep learning methods on structure sets by integrating multimodal data compiled from the radiotherapy centers of the US Veterans Health Administration (VHA) and Virginia Commonwealth University (VCU). These de-identified data comprise 16,290 prostate structures. Our method integrates the multimodal textual and imaging data with Convolutional Neural Network (CNN)-based deep learning approaches such as CNN, Visual Geometry Group (VGG) network, and Residual Network (ResNet) and shows improved results in prostate radiotherapy structure name standardization. Evaluation with macro-averaged F1 score shows that our model with single-modal textual data usually performs better than previous studies. The models perform well on textual data alone, while the addition of imaging data shows that deep neural networks achieve better performance using information present in other modalities. Additionally, using masked images and masked doses along with text leads to an overall performance improvement with the CNN-based architectures than using all the modalities together. Undersampling the majority class leads to further performance enhancement. The VGG network on the masked image-dose data combined with CNNs on the text data performs the best and presents the state-of-the-art in this domain.

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“…In 2022, Jinkang Wang et al [10] proposed an underwater image semantic segmentation method to precisely segment targets; however, the first step in this method was to improve image quality by performing image enhancement operations based on multispatial transformation. In recent years, increasing numbers of researchers have begun to improve segmentation accuracy from the perspective of integrating multiscale features of fish targets, such as the multiscale CNN network [11][12][13][14] and the porous GAN network [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Underwater Fish Segmentation Algorithm Based on Improved PSPNet Network

Han,

Zheng,

Kong

et al. 2023

Sensors

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With the sustainable development of intelligent fisheries, accurate underwater fish segmentation is a key step toward intelligently obtaining fish morphology data. However, the blurred, distorted and low-contrast features of fish images in underwater scenes affect the improvement in fish segmentation accuracy. To solve these problems, this paper proposes a method of underwater fish segmentation based on an improved PSPNet network (IST-PSPNet). First, in the feature extraction stage, to fully perceive features and context information of different scales, we propose an iterative attention feature fusion mechanism, which realizes the depth mining of fish features of different scales and the full perception of context information. Then, a SoftPool pooling method based on fast index weighted activation is used to reduce the numbers of parameters and computations while retaining more feature information, which improves segmentation accuracy and efficiency. Finally, a triad attention mechanism module, triplet attention (TA), is added to the different scale features in the golden tower pool module so that the space attention can focus more on the specific position of the fish body features in the channel through cross-dimensional interaction to suppress the fuzzy distortion caused by background interference in underwater scenes. Additionally, the parameter-sharing strategy is used in this process to make different scale features share the same learning weight parameters and further reduce the numbers of parameters and calculations. The experimental results show that the method presented in this paper yielded better results for the DeepFish underwater fish image dataset than other methods, with 91.56% for the Miou, 46.68 M for Params and 40.27 G for GFLOPS. In the underwater fish segmentation task, the method improved the segmentation accuracy of fish with similar colors and water quality backgrounds, improved fuzziness and small size and made the edge location of fish clearer.

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Image semantic segmentation method based on GAN network and ENet model

Cited by 11 publications

References 30 publications

Stripe Extraction of Oceanic Internal Waves Using PCGAN with Small-Data Training

Stripe Extraction of Oceanic Internal Waves Using PCGAN with Small-Data Training

Multimodal Deep Learning Methods on Image and Textual Data to Predict Radiotherapy Structure Names

Underwater Fish Segmentation Algorithm Based on Improved PSPNet Network

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