Enhancing image processing architecture using deep learning for embedded vision systems

被引：34

作者：

Udendhran, R. ^{[1
]}

Balamurugan, M. ^{[1
]}

Suresh, A. ^{[2
]}

Varatharajan, R. ^{[3
]}

机构：

[1] Bharathidasan Univ, Dept Comp Sci, Trichy, India

[2] Nehru Inst Engn & Technol, Dept Comp Sci & Engn, Coimbatore, Tamil Nadu, India

[3] Sri Ramanujar Engn Coll, Dept Elect & Commun Engn, Chennai 600127, Tamil Nadu, India

来源：

MICROPROCESSORS AND MICROSYSTEMS | 2020年 / 76卷

关键词：

Deep learning; Embedded vision systems; Embedded systems; Image processing; Feature extraction; Convolutional neural networks; Google inception network; BREAST-CANCER MORTALITY; DESIGN;

D O I：

10.1016/j.micpro.2020.103094

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In recent years, the success and capabilities of embedded vision have showed up in embedded applications. The embedding of vision into electronic devices such as embedded medical applications is being driven by the availability of high-performance processors, integrating with deep learning algorithms, as well as advances in image processing technology. But, including image processing in embedded vision systems need huge amount of computational capabilities even to process a single image to detect an object and it's extremely challenging to implement in embedded systems. Implementing deep learning algorithms and testing it on a task specific data set could provide enhanced results. In this paper, an approach for enhancing image processing architecture using deep learning for embedded vision systems is proposed and analyzed. Implementing deep learning algorithms and testing it on embedded vision yielded effective results. (C) 2020 Elsevier B.V. All rights reserved.

引用

页数：8

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