Detailed Information

Cited 0 time in webofscience Cited 5 time in scopus
Metadata Downloads

Distributed deep learning platform for pedestrian detection on IT convergence environment

Authors
Han, Seong-SooKim, Yoon-KiJeon, You-BooPark, JinSooPark, Doo-SoonHwang, DuHyunJeong, Chang-Sung
Issue Date
Jul-2020
Publisher
Kluwer Academic Publishers
Keywords
IT convergence; Pedestrian detection; Faster R-CNN; Deep learning; Distribution processing; Parallel processing; Virtual machine
Citation
Journal of Supercomputing, v.76, no.7, pp 5460 - 5485
Pages
26
Journal Title
Journal of Supercomputing
Volume
76
Number
7
Start Page
5460
End Page
5485
URI
https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/2700
DOI
10.1007/s11227-020-03195-0
ISSN
0920-8542
1573-0484
Abstract
IT technology and traditional industries have been combined recently, resulting in IT convergence technology in various fields. Through convergence with the automobile, pedestrian detection technology, in particular, is used in the autonomous navigation control service of autonomous vehicles and also applied in various fields such as intelligent CCTV and robot recognition technology. For pedestrian detection, hierarchical classification and feature vector were used in early stage, and deep learning is under active progress. However, since deep learning for pedestrian detection is time-consuming for processing a large volume of image data, it requires a lot of computing resources, and hence building such a system is very expensive. Therefore, in this paper we shall present a distributed deep learning platform which can easily build a cluster, and execute deep learning process in the distributed cloud environment, while achieving performance improvement in various ways. Our platform provides a convenient interface for easily and efficiently executing the deep learning process in a distributed environment by providing a multilayered system architecture. Our system builds and utilizes computing power in easy and efficient way by leveraging container technique, so-called OS-level virtualization, rather than traditional hypervisor-based virtualization. In our system, we improve the whole performance by exploiting both of data and parameter parallelisms at once and reduce the synchronization overhead by exploiting asynchronous communication for parameter updates. Also, we propose an efficient resource allocation scheme for parameter servers and slaves which can improve the performance from the experiment.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Engineering > Department of Computer Software Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Park, Jin Soo photo

Park, Jin Soo
Industry-University Cooperation Foundation
Read more

Altmetrics

Total Views & Downloads

BROWSE