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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Huynh-Nhu Truong
Ba Ria-Vung Tau College of Technology
Computer Science & IT

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Applying convolutional neural networks for limited-memory application Xuan-Kien Dang; Huynh-Nhu Truong; Viet-Chinh Nguyen; Thi-Duyen-Anh Pham
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 1: February 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i1.16232

Abstract

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.
Co-Authors Thi-Duyen-Anh Pham Viet-Chinh Nguyen Xuan-Kien Dang