Semantic image segmentation network based on deep learning

2020 ◽  
Author(s):  
Bo Chen ◽  
Jiahao Zhang ◽  
Jianbang Zhou ◽  
Zhong Chen ◽  
Jian Yang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Semantic Image Segmentation

scholarly journals Review and Evaluation of Deep Learning Architectures for Efficient Land Cover Mapping with UAS Hyper-Spatial Imagery: A Case Study Over a Wetland

2020 ◽  
Vol 12 (6) ◽  
pp. 959 ◽  
Author(s):  
Mohammad Pashaei ◽  
Hamid Kamangir ◽  
Michael J. Starek ◽  
Philippe Tissot
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Land Cover ◽  
Spatial Resolution ◽  
Unmanned Aircraft ◽  
High Temporal Resolution ◽  
Semantic Image Segmentation ◽  
Image Labeling ◽  
Training Samples ◽  
Learning Architectures

Deep learning has already been proved as a powerful state-of-the-art technique for many image understanding tasks in computer vision and other applications including remote sensing (RS) image analysis. Unmanned aircraft systems (UASs) offer a viable and economical alternative to a conventional sensor and platform for acquiring high spatial and high temporal resolution data with high operational flexibility. Coastal wetlands are among some of the most challenging and complex ecosystems for land cover prediction and mapping tasks because land cover targets often show high intra-class and low inter-class variances. In recent years, several deep convolutional neural network (CNN) architectures have been proposed for pixel-wise image labeling, commonly called semantic image segmentation. In this paper, some of the more recent deep CNN architectures proposed for semantic image segmentation are reviewed, and each model’s training efficiency and classification performance are evaluated by training it on a limited labeled image set. Training samples are provided using the hyper-spatial resolution UAS imagery over a wetland area and the required ground truth images are prepared by manual image labeling. Experimental results demonstrate that deep CNNs have a great potential for accurate land cover prediction task using UAS hyper-spatial resolution images. Some simple deep learning architectures perform comparable or even better than complex and very deep architectures with remarkably fewer training epochs. This performance is especially valuable when limited training samples are available, which is a common case in most RS applications.

Deep-learning-based semantic image segmentation of graphene field-effect transistors

2021 ◽  
Vol 14 (3) ◽  
pp. 036504
Author(s):  
Shota Ushiba ◽  
Naruto Miyakawa ◽  
Naoya Ito ◽  
Ayumi Shinagawa ◽  
Tomomi Nakano ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Field Effect ◽  
Field Effect Transistors ◽  
Semantic Image Segmentation

SegFast-V2: Semantic image segmentation with less parameters in deep learning for autonomous driving

2019 ◽  
Vol 10 (11) ◽  
pp. 3145-3154 ◽  
Author(s):  
Swarnendu Ghosh ◽  
Anisha Pal ◽  
Shourya Jaiswal ◽  
K. C. Santosh ◽  
Nibaran Das ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Autonomous Driving ◽  
Semantic Image Segmentation

scholarly journals Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey

2021 ◽  
Vol 11 (19) ◽  
pp. 8802
Author(s):  
Ilias Papadeas ◽  
Lazaros Tsochatzidis ◽  
Angelos Amanatiadis ◽  
Ioannis Pratikakis
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Real Time ◽  
Current Trend ◽  
Autonomous Driving ◽  
Evaluation Framework ◽  
Future Research ◽  
Comprehensive Overview ◽  
Semantic Image Segmentation ◽  
Recent Developments

Semantic image segmentation for autonomous driving is a challenging task due to its requirement for both effectiveness and efficiency. Recent developments in deep learning have demonstrated important performance boosting in terms of accuracy. In this paper, we present a comprehensive overview of the state-of-the-art semantic image segmentation methods using deep-learning techniques aiming to operate in real time so that can efficiently support an autonomous driving scenario. To this end, the presented overview puts a particular emphasis on the presentation of all those approaches which permit inference time reduction, while an analysis of the existing methods is addressed by taking into account their end-to-end functionality, as well as a comparative study that relies upon a consistent evaluation framework. Finally, a fruitful discussion is presented that provides key insights for the current trend and future research directions in real-time semantic image segmentation with deep learning for autonomous driving.

scholarly journals Portrait Segmentation Using Ensemble of Heterogeneous Deep-Learning Models

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 197
Author(s):  
Yong-Woon Kim ◽  
Yung-Cheol Byun ◽  
Addapalli V. N. Krishna
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Medical Image Analysis ◽  
Learning Technology ◽  
Learning Models ◽  
Computing Power ◽  
Semantic Image Segmentation ◽  
Voting Method ◽  
Segmentation Models

Image segmentation plays a central role in a broad range of applications, such as medical image analysis, autonomous vehicles, video surveillance and augmented reality. Portrait segmentation, which is a subset of semantic image segmentation, is widely used as a preprocessing step in multiple applications such as security systems, entertainment applications, video conferences, etc. A substantial amount of deep learning-based portrait segmentation approaches have been developed, since the performance and accuracy of semantic image segmentation have improved significantly due to the recent introduction of deep learning technology. However, these approaches are limited to a single portrait segmentation model. In this paper, we propose a novel approach using an ensemble method by combining multiple heterogeneous deep-learning based portrait segmentation models to improve the segmentation performance. The Two-Models ensemble and Three-Models ensemble, using a simple soft voting method and weighted soft voting method, were experimented. Intersection over Union (IoU) metric, IoU standard deviation and false prediction rate were used to evaluate the performance. Cost efficiency was calculated to analyze the efficiency of segmentation. The experiment results show that the proposed ensemble approach can perform with higher accuracy and lower errors than single deep-learning-based portrait segmentation models. The results also show that the ensemble of deep-learning models typically increases the use of memory and computing power, although it also shows that the ensemble of deep-learning models can perform more efficiently than a single model with higher accuracy using less memory and less computing power.

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