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A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5
2021
Remote Sensing
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Deep-learning-based object detection algorithms have significantly improved the performance of wheat spike detection. ...
The network is rebuilt by adding a microscale detection layer, setting prior anchor boxes, and adapting the confidence loss function of the detection layer based on the IoU (Intersection over Union). ...
State-of-the-art deep learning object detection algorithms have made significant progress in wheat spike detection in images [34, 35] . ...
doi:10.3390/rs13163095
fatcat:y7wxffejrfe43bhsz2bypdsmdm
Geometrical and Deep Learning Approaches for Instance Segmentation of CFRP Fiber Bundles in Textile Composites
2021
Composite structures
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One is based on the geometrical analysis of the material structure using conventional image analysis; the other is based on the deep learning prediction of ideal inputs for segmentation based on the watershed ...
The deep learning-based method is trained using randomly generated synthetic images of a woven composite material, which avoids an expensive human annotation step. ...
The Special Research Fund of Ghent University is acknowledged for the financial support for M.N.B under project number BOF17-GOA-015. ...
doi:10.1016/j.compstruct.2021.114626
fatcat:usb7sq4vyfbe3iejo5m3r4pko4
Artificial Intelligence-driven Image Analysis of Bacterial Cells and Biofilms
[article]
2021
arXiv
pre-print
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We adapt two deep learning models: (a) a deep convolutional neural network (DCNN) model to achieve semantic segmentation of the cells, (d) a mask region-convolutional neural network (Mask R-CNN) model ...
to achieve instance segmentation of the cells. ...
Here we demonstrate the ability of deep learning combined with image processing algorithms to extract the microscale geometric features of biofilms. ...
arXiv:2112.01577v1
fatcat:2shemv4vwrhg5nlszji4yjykbu
The Adoption of Image-Driven Machine Learning for Microstructure Characterization and Materials Design: A Perspective
[article]
2021
arXiv
pre-print
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segmentation model on electron microscopy images, the prevalence of transfer learning in the domain, etc. ...
Finally, we discuss the importance of interpretability and explainability, and provide an overview of two emerging techniques in the field: semantic segmentation and generative adversarial networks. ...
sectioning images Strohmann2019 [18] Semantic segmentation of 3D microstructure of Al-Si Evsevleev2020 [19] Deep-learning based semantic segmentation of individual phases from synchrotron x-ray computed ...
arXiv:2105.09729v1
fatcat:vlmq3cm2fnhflomfl6j6oupnse
Automated processing of X-ray computed tomography images via panoptic segmentation for modeling woven composite textiles
[article]
2022
arXiv
pre-print
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This effort represents the first deep learning based automated process for segmenting unique yarn instances in a woven composite textile. ...
A new, machine learning-based approach for automatically generating 3D digital geometries of woven composite textiles is proposed to overcome the limitations of existing analytical descriptions and segmentation ...
As a result, the first deep learning based automated process for segmenting unique yarn instances in a woven composite textile is presented. ...
arXiv:2202.01265v1
fatcat:ugsnzsvzujhwbe7blw6lx4jgki
Street View Imagery (SVI) in the Built Environment: A Theoretical and Systematic Review
2022
Buildings
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(II) Currently, SVI is functional and valuable for quantifying the built environment, spatial sentiment perception, and spatial semantic speculation. ...
A notable trend is the application of SVI towards a focus on the perceptions of the built environment, which provides a more refined and effective way to depict urban forms in terms of physical and social ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/buildings12081167
fatcat:7sdadsypufbktpyqanhc2allrm
2021 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 14
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. ...
., +, JSTARS 2021 8407-8418 Wide-Area Land Cover Mapping With Sentinel-1 Imagery Using Deep Learning Semantic Segmentation Models. ...
., +, JSTARS 2021 10314-10335 Wide-Area Land Cover Mapping With Sentinel-1 Imagery Using Deep Learning Semantic Segmentation Models. ...
doi:10.1109/jstars.2022.3143012
fatcat:dnetkulbyvdyne7zxlblmek2qy
A Deep Learning Perspective on Dropwise Condensation
2021
Advanced Science
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Here, an intelligent vision-based framework is demonstrated that unites classical thermofluidic imaging techniques with deep learning to fundamentally address this challenge. ...
The deep learning framework can autonomously harness physical descriptors and quantify thermal performance at extreme spatio-temporal resolutions of 300 nm and 200 ms, respectively. ...
N.M. gratefully acknowledges funding support from the National Science Foundation under Grant No. 1554249, the Office of Naval Research (ONR) under grant No. ...
doi:10.1002/advs.202101794
pmid:34561960
pmcid:PMC8596129
fatcat:rctmgfbdjrgrtnsmfygktmqdoq
Crowded Scene Analysis: A Survey
2015
IEEE transactions on circuits and systems for video technology (Print)
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2015 pre-print arXiv:1502.01812v1 fatcat:frsqmw6dgjgajln7eojve7bnjy
In the past few years, an increasing number of works on crowded scene analysis have been reported, covering different aspects including crowd motion pattern learning, crowd behavior and activity analysis ...
However, the visual occlusions and ambiguities in crowded scenes, as well as the complex behaviors and scene semantics, make the analysis a challenging task. ...
The RFT model has been applied in semantic region analysis in crowded scenes in Zhou et al. [7] , [8] , based on the motions of objects. ...
doi:10.1109/tcsvt.2014.2358029
fatcat:prgoh37gjfcl7n6dp2u6tsdoda
Divide-and-Attention Network for HE-Stained Pathological Image Classification
2022
Biology
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The DANet utilizes a deep-learning method to decompose a pathological image into nuclei and non-nuclei parts. ...
In addition, we introduce deep canonical correlation analysis (DCCA) constraints in the feature fusion process of different branches. ...
Conflicts of Interest: 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/biology11070982
fatcat:c3t7mudihvb35f2d52fv6itcf4
Artificial Intelligence as a Tool to Study the 3D Skeletal Architecture in Newly Settled Coral Recruits: Insights into the Effects of Ocean Acidification on Coral Biomineralization
2022
Journal of Marine Science and Engineering
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Deep-learning neural networks were invoked to explore AI segmentation of these regions, to overcome limitations of common segmentation techniques. ...
By imaging the corals with PCE-CT, we revealed the interwoven morphologies of RADs and TDs. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/jmse10030391
fatcat:yznrgnbq3bcolhhx4hehcogrle
Rock mass structural surface trace extraction based on transfer learning
2022
Open Geosciences
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Transfer learning can abstract high-level features from low-level features with a small number of training samples, which can automatically express the inherent characteristics of objects. ...
This article proposed a rock mass structural surface trace extraction method based on the transfer learning technique that considers the attention mechanism and shape constraints. ...
All authors have read and agreed to the published version of the manuscript. ...
doi:10.1515/geo-2022-0337
fatcat:q7gnq3xbgvgahdaeyc637p7f74
Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs
[article]
2021
arXiv
pre-print
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In this work, we perform semantic segmentation of multiple defect types in electron microscopy images of irradiated FeCrAl alloys using a deep learning Mask Regional Convolutional Neural Network (Mask ...
modeling and understanding of irradiated Fe-based materials properties. ...
transferable than deep learning-based methods. ...
arXiv:2110.08244v1
fatcat:s7qaovfr6jf5vkqdkolmp5cbra
A deep learning approach for pose estimation from volumetric OCT data
2018
Medical Image Analysis
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2018 pre-print arXiv:1803.03852v1 fatcat:dadp7c3jk5fknjgnx3z77xabuq
We address pose estimation from OCT volume data with a new deep learning-based tracking framework. ...
We use this setup to provide an in-depth analysis on deep learning-based pose estimation from volumes. ...
) and semantic segmentation (Long et al., 2015) . ...
doi:10.1016/j.media.2018.03.002
pmid:29550582
fatcat:5jwsfzvbsbetpkgaexmr636tre
DeepFoci: Deep Learning-Based Algorithm for Fast Automatic Analysis of DNA Double Strand Break Ionizing Radiation-Induced Foci
[article]
2020
bioRxiv
pre-print
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In this study, we introduce DeepFoci - a deep learning-based fully-automatic method for IRIF counting and its morphometric analysis. ...
IRIF segmentation. ...
We developed a new method based on deep learning that overcomes many of the current limitations of the image analysis and allows rapid and automated quantification and parameter evaluation of IRIF foci ...
doi:10.1101/2020.10.07.321927
fatcat:l2jaxlajrnf7lfo6nczitv57uy
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