Applying convolutional neural networks for limited-memory application

Xuan-Kien Dang (Ho Chi Minh City University of Transport)
Huynh-Nhu Truong (Ba Ria-Vung Tau College of Technology)
Viet-Chinh Nguyen (Ho Chi Minh City University of Transport)
Thi-Duyen-Anh Pham (Ho Chi Minh University of Transport)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 19, No 1: February 2021

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.