TELKOMNIKA Telecommunication, Computing, Electronics and Control
Applying convolutional neural networks for limited-memory application
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Applying convolutional neural networks for limited-memory application
Applying convolutional neural networks for limited-memory application
Subject
Convolutional neural networks
Image processing
Limited hardware devices
Maritime application
Object classification
Image processing
Limited hardware devices
Maritime application
Object classification
Description
Currently, convolutional neural networks (CNN) are considered as the most
effective tool in image diagnosis and processing techniques. In this paper, we
studied and applied the modified SSDLite_MobileNetV2 and proposed a
solution to always maintain the boundary of the total memory capacity in the
following robust bound and applied on the bridge navigational watch & alarm
system (BNWAS). The hardware was designed based on raspberry Pi-3, an
embedded single board computer with CPU smartphone level, limited RAM
without CUDA GPU. Experimental results showed that the deep learning
model on an embedded single board computer brings us high effectiveness in
application.
effective tool in image diagnosis and processing techniques. In this paper, we
studied and applied the modified SSDLite_MobileNetV2 and proposed a
solution to always maintain the boundary of the total memory capacity in the
following robust bound and applied on the bridge navigational watch & alarm
system (BNWAS). The hardware was designed based on raspberry Pi-3, an
embedded single board computer with CPU smartphone level, limited RAM
without CUDA GPU. Experimental results showed that the deep learning
model on an embedded single board computer brings us high effectiveness in
application.
Creator
Xuan-Kien Dang, Huynh-Nhu Truong, Viet-Chinh Nguyen, Thi-Duyen-Anh Pham
Source
http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
Convolutional neural networks
Image processing
Limited hardware devices
Maritime application
Object classification
Image processing
Limited hardware devices
Maritime application
Object classification
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Xuan-Kien Dang, Huynh-Nhu Truong, Viet-Chinh Nguyen, Thi-Duyen-Anh Pham, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Applying convolutional neural networks for limited-memory application,” Repository Horizon University Indonesia, accessed November 22, 2024, https://repository.horizon.ac.id/items/show/3553.
Applying convolutional neural networks for limited-memory application,” Repository Horizon University Indonesia, accessed November 22, 2024, https://repository.horizon.ac.id/items/show/3553.