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GSCA-UNet: Towards Automatic Shadow Detection in Urban Aerial Imagery with Global-Spatial-Context Attention Module
2020
Remote Sensing
Preserved Fulltext
Unfortunately, in general, automatic shadow detection methods for urban aerial images cannot achieve satisfactory performance due to the limitation of feature patterns and the lack of consideration of ...
The performance of the proposed method was evaluated on several typical urban aerial images. ...
Conflicts of Interest: The authors declare no conflicts of interest.
Abbreviations The following abbreviations are used in this manuscript. ...
doi:10.3390/rs12172864
fatcat:bqkhixmkzzekrbwhazwya2ve3q
A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging
2021
Applied Sciences
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Through the increasing use of unmanned aerial vehicles as remote sensing tools, shadows become evident in aerial imaging; this fact, alongside the higher spatial resolution obtained by high-resolution ...
The quantitative comparison was executed by using the shadow standard deviation index (SSDI), where the proposed work provided low values that improve up to 19 units regarding other tested methods. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app112311494
fatcat:l4dsnjaxtjdmhhnn7tsxa27vmy
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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Guo, Q., +, TIP 2020 2999-3013
Decision support systems
Improving Dataset Volumes and Model Accuracy With Semi-Supervised
Iterative Self-Learning. ...
Li, X., +, Self-Enhanced Convolutional Network for Facial Video Hallucination. Fang, C., +, TIP 2020 3078-3090 Self-Supervised Learning of Detailed 3D Face Reconstruction. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
UG^2+ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments
[article]
2020
arXiv
pre-print
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In its second track, we focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. ...
To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and low-light conditions, respectively, with annotate objects/faces annotated ...
(Semi-)Supervised Face Detection in the Low Light Condition In Sub-challenge 2.2, we use our self-curated DARK FACE dataset. ...
arXiv:1904.04474v4
fatcat:j4gz3l776bezpb73iaclny4n74
Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images
2021
Sensors
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We propose an improved Mask R-CNN model, called SCMask R-CNN, to enhance the detection effect in the high-resolution remote sensing images which contain the dense targets and complex background. ...
This model uses a modified SC-conv based on the ResNet101 backbone network to obtain more discriminative feature information and adds a set of dilated convolutions with a specific size to improve the instance ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s21082618
pmid:33917904
fatcat:tqae6jncgraexg6hd32oq5zb3a
A semi-supervised self-training method to develop assistive intelligence for segmenting multiclass bridge elements from inspection videos
2021
Structural Health Monitoring
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2021 pre-print arXiv:2109.05078v1 fatcat:mysauhrysbdhjfltkxlofl372mOther Versions
Furthermore, a semi-supervised self-training method was developed to engage experienced inspectors in refining the network iteratively. ...
This article is motivated to develop an assistive intelligence model for segmenting multiclass bridge elements from the inspection videos captured by an aerial inspection platform. ...
Acknowledgements Research presented in this paper is partially supported by the U.S. ...
doi:10.1177/14759217211010422
fatcat:6kzlh7yluzdb5fcj7z4dhk3lnu
2021 Index IEEE Transactions on Image Processing Vol. 30
2021
IEEE Transactions on Image Processing
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Note that the item title is found only under the primary entry in the Author Index. ...
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021. ...
., +, TIP 2021 3179-3191 EnlightenGAN: Deep Light Enhancement Without Paired Supervision. ...
doi:10.1109/tip.2022.3142569
fatcat:z26yhwuecbgrnb2czhwjlf73qu
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study
[article]
2020
arXiv
pre-print
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We introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation, the MiniFrance suite. ...
The development of semi-supervised learning techniques is essential to enhance the generalization capacities of machine learning algorithms. ...
The authors acknowledge the IGN for providing the BD ORTHO database under Open Licence v1.0 (https://www.etalab.gouv.fr/ licence-ouverte-open-licence) and the European Copernicus Program for providing ...
arXiv:2010.07830v1
fatcat:h7k5dnh5nnhffl2iy4o6yg67du
Target Detection and Segmentation in Circular-Scan Synthetic-Aperture-Sonar Images using Semi-Supervised Convolutional Encoder-Decoders
[article]
2021
arXiv
pre-print
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The encoder portion of the MB-CEDN extracts visual contrast features from CSAS images. These features are fed into dual decoders that perform pixel-level segmentation to mask targets. ...
We compare against existing approaches from the computer-vision literature. We show that our framework outperforms supervised, deep-saliency networks designed for natural imagery. ...
The chosen color scheme is such that dark shades correspond to low aggregate acoustic returns and light shades to high returns. ...
arXiv:2101.03603v3
fatcat:plz7jnctrvcvjjmtovhoj6tjsq
Isotropic surround suppression and Hough transform based target recognition from aerial images
2017
International Journal of Advanced and Applied Sciences
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Keeping this in view, in this study image segmentation is performed using canny edge detection technique. ...
The results indicate that the new method is efficient and effective for extracting target in optical images acquired by Unmanned Air Vehicles and it improves target detection significantly. ...
However, when seen from afar, the bridge appears as a thick dark line. However, the background complexity is enhanced in terms of piers and linked cables. ...
doi:10.21833/ijaas.2017.08.006
fatcat:tvypcmzuprbjfg7k7bxsdu57fi
Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey
2022
Remote Sensing
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With the development of deep learning (DL) technology, which has accelerated in recent years, numerous intelligent and efficient detection algorithms have been proposed. ...
However, although some scholars have authored reviews on DL-based object detection systems, the leading DL-based object detection improvement strategies have never been summarized in detail. ...
This nonlinear transformation enhances the details of the image by brightening dark areas and reducing the brightness of light areas. ...
doi:10.3390/rs14102385
fatcat:sgoqy33cdbe2xopqqfsa2hyowq
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
2020
Patterns
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With widespread applications of artificial intelligence (AI), the capabilities of the perception, understanding, decision-making, and control for autonomous systems have improved significantly in recent ...
Accuracy means that a well-trained model shows good results during the testing phase, in which the testing set shares a same task or a data distribution with the training set. ...
ACKNOWLEDGMENTS The authors would like to thank the Editor-in-Chief, Scientific Editor, and anonymous referees for their helpful comments and suggestions, which have greatly improved this paper. ...
doi:10.1016/j.patter.2020.100050
pmid:33205114
pmcid:PMC7660378
fatcat:vs7wm2yrwjamjbaml36663wvze
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
[article]
2020
arXiv
pre-print
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With widespread applications of artificial intelligence (AI), the capabilities of the perception, understanding, decision-making and control for autonomous systems have improved significantly in the past ...
Accuracy means that a well-trained model shows good results during the testing phase, in which the testing set shares a same task or a data distribution with the training set. ...
[156] tackled this problem by introducing a bidirectional learning framework with self-supervised learning, in which both translation and segmentation adaption models can promote each other in a closed ...
arXiv:2003.12948v3
fatcat:qtmjs74p2vh6thdotbhgebdvoi
One-Shot Learning with Pseudo-Labeling for Cattle Video Segmentation in Smart Livestock Farming
2022
Animals
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In order to reduce the reliance on the number of labeled images, one-shot learning with a pseudo-labeling approach is proposed using only one labeled image frame to segment animals in videos. ...
Then, PL leverages the segmentation results of the Xception-FCN model to fine-tune the model, leading to performance boosts in cattle video segmentation. ...
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. ...
doi:10.3390/ani12050558
pmid:35268130
pmcid:PMC8908826
fatcat:it4mllkwp5bv5mzcxv42wf7goe
Table of contents
2020
IEEE Transactions on Image Processing
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An 4323 Improving Dataset Volumes and Model Accuracy With Semi-Supervised Iterative Self-Learning ........................ ........................................................................... ...
Lin 5289 Outdoor RGBD Instance Segmentation With Residual Regretting Learning .......... Z. Xu, S. Liu, J. Shi, and C. ...
doi:10.1109/tip.2019.2940372
fatcat:h23ul2rqazbstcho46uv3lunku
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