Enhancing image processing architecture using deep learning for embedded vision systems |
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| Affiliation: | 1. Department of Computer Science, Bharathidasan University, Trichy, India;2. Department of Computer Science, Bharathidasan University, Trichy, India;3. Department of Computer Science and Engineering, Nehru Institute of Engineering and Technology, Coimbatore, India;4. Department of Electronics and Communication Engineering, Sri Ramanujar Engineering College, Chennai- 600127, Tamil Nadu, India;1. School of Computer Science and Information Engineering, Anyang Institute of Technology, Henan, Anyang 455000, China;2. School of Management, Xinxiang Medical University, Henan, Xinxiang 453003, China;1. Department for Resilience of Maritime Systems, German Aerospace Center, Bremerhaven, Germany;2. Universidade Federal de Viçosa, Brazil;3. Group of Computer Architecture, University of Bremen, Germany;4. Universidade Federal de Minas Gerais, Brazil;5. Centro Federal de Educação Tecnológica de Minas Gerais, Brazil;6. Johannes Kepler University Linz, Austria;7. Cyber-Physical Systems, DFKI GmbH, Bremen, Germany;1. Department of Electrical and Computer Engineering, Kharazmi University, Tehran, Iran;2. Mälardalen University, Sweden |
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| Abstract: | 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. |
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