Generating Synthetic Data for Deep Learning using VR Digital Twin
5th International Conference on Cloud and Big Data Computing, ICCBDC 2021
; : 52-56, 2021.
Article
in English
| Scopus | ID: covidwho-1596232
ABSTRACT
The ongoing Covid-19 pandemic has made it challenging for large scale data collection, in particular for Convolutional Neural Network (CNN)-based computer vision systems. Additionally, there are numerous circumstances where security, privacy, and limitations pertaining to the accessibility of the required equipment make it arduous to validate computer vision systems with real-world datasets. In this paper, we investigated the possibilities of using synthetic datasets, generated from Virtual Environments (VE) for training and validation of CNN models. We present two use cases where the above-mentioned circumstances play a vital role in preparing the datasets and validating the model with large-scale datasets. By developing and leveraging a three-dimensional Digital Twin (DT), we produce large scale datasets for validating social distancing in workspaces;and in the context of semi-autonomous vehicles, we evaluate how a CNN-based object detection model would perform in an Indian road scenario. © 2021 ACM.
CNN; Digital, Twins; Object, Detection; Synthetic, Data; Autonomous, vehicles; Computer, vision; Convolutional, neural, networks; E-learning; Large, dataset; Object, recognition; Computer, vision, system; Convolutional, neural, network; Data, collection; Large, scale, data; Large-scale, datasets; Network-based; Objects, detection; Real-world, datasets; Security/privacy; Deep, learning
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
5th International Conference on Cloud and Big Data Computing, ICCBDC 2021
Year:
2021
Document Type:
Article
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