GDL

Journal from gdlhub / 2021-09-04 15:47:59
Oleh : Xuan-Kien Dang, Huynh-Nhu Truong, Viet-Chinh Nguyen, Thi-Duyen-Anh Pham, Telkomnika
Dibuat : 2021-02-04, dengan 1 file

Keyword : convolutional neural networks; image processing; limited hardware devices; maritime application; object classification;
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/16232
Sumber pengambilan dokumen : web

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.

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