A Brief Survey on Semantic Segmentation with Deep Learning

Hao, Shijie; Zhou, Yuan; Guo, Yanrong

doi:10.1016/j.neucom.2019.11.118

Cited by 283 publications

(93 citation statements)

References 46 publications

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“…To evaluate the performance of the deep learning model, the mean pixel accuracy (MPA) and mean intersection over union (MIoU) of the testing set were calculated 30 . The pixel accuracy is the ratio of the number of correctly classified pixels to the total pixel number.…”

Section: Deep Learning Methodologymentioning

confidence: 99%

Identification of voids and interlaminar shear strengths of polymer‐matrix composites by optical microscopy experiment and deep learning methodology

Luo

Zhang

Lei

et al. 2021

Polymers for Advanced Techs

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The internal void defects induced during the manufacturing process of polymer‐matrix composites can significantly degrade the mechanical properties of the composite, particularly the interlaminar shear strength (ILSS). In this study, we developed an innovative integrated methodology based on a deep learning semantic segmentation algorithm, named DeepLabV3+, and a theoretically driven equation to automatically identify voids in optical images and investigate the relationship between the microscopic voids and macroscopic ILSS parameters of the composite laminates. Results suggest that for the best fine‐tuned DeepLabV3+ framework, the corresponding mean pixel accuracy and intersection over union scores on the testing set were 99.84% and 90.82%, respectively, thereby indicating the potential of the generalized trained model. In addition, detailed experiments revealed that the proposed method can successfully obtain the ILSS values of laminates with different void contents. In addition, the ILSS values of the carbon/epoxy laminates decreased by approximately 27% with an increase in the void content from 0.07% to 3.14%.

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Section: Deep Learning Methodologymentioning

confidence: 99%

Identification of voids and interlaminar shear strengths of polymer‐matrix composites by optical microscopy experiment and deep learning methodology

Luo

Zhang

Lei

et al. 2021

Polymers for Advanced Techs

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“…Dense skip connections were introduced in the DenseNet [38], and have been widely used in many segmentation models [15]. Please refer to [15], [39], [40] for comprehensive reviews of recent improvements of the FCN models for semantic segmentation of natural and medical images.…”

Section: B Deep Network For Image Segmentationmentioning

confidence: 99%

Medical Image Segmentation With Limited Supervision: A Review of Deep Network Models

Wang

2021

IEEE Access

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Despite the remarkable performance of deep learning methods on various tasks, most cutting-edge models rely heavily on large-scale annotated training examples, which are often unavailable for clinical and health care tasks. The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling. Therefore, the strong capability of learning and generalizing from limited supervision, including a limited amount of annotations, sparse annotations, and inaccurate annotations, is crucial for the successful application of deep learning models in medical image segmentation. However, due to its intrinsic difficulty, segmentation with limited supervision is challenging and specific model design and/or learning strategies are needed. In this paper, we provide a systematic and up-to-date review of the solutions above, with summaries and comments about the methodologies. We also highlight several problems in this field, discussed future directions observing further investigations.

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“… Lateef and Ruichek (2019) provide a systematic and exhaustive review of different categories of approaches employing Deep Learning techniques and presenting available benchmarks and datasets as well as evaluation criteria. Hao et al (2020) put a different focus on the degree of supervision during training. More traditional approaches are summarized in Thoma (2016) and recent advances are briefly addressed in Atif et al (2019) and Minaee et al (2020) .…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Understanding Satellite Imagery: An Experimental Survey

Mohanty

Czakon²,

Kaczmarek

et al. 2020

Front. Artif. Intell.

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Translating satellite imagery into maps requires intensive effort and time, especially leading to inaccurate maps of the affected regions during disaster and conflict. The combination of availability of recent datasets and advances in computer vision made through deep learning paved the way toward automated satellite image translation. To facilitate research in this direction, we introduce the Satellite Imagery Competition using a modified SpaceNet dataset. Participants had to come up with different segmentation models to detect positions of buildings on satellite images. In this work, we present five approaches based on improvements of U-Net and Mask R-Convolutional Neuronal Networks models, coupled with unique training adaptations using boosting algorithms, morphological filter, Conditional Random Fields and custom losses. The good results—as high as AP=0.937 and AR=0.959—from these models demonstrate the feasibility of Deep Learning in automated satellite image annotation.

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A Brief Survey on Semantic Segmentation with Deep Learning

Cited by 283 publications

References 46 publications

Identification of voids and interlaminar shear strengths of polymer‐matrix composites by optical microscopy experiment and deep learning methodology

Identification of voids and interlaminar shear strengths of polymer‐matrix composites by optical microscopy experiment and deep learning methodology

Medical Image Segmentation With Limited Supervision: A Review of Deep Network Models

Deep Learning for Understanding Satellite Imagery: An Experimental Survey

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