Indiana University-Purdue University Indianapolis (IUPUI) / The word “Perception” seems to be intuitive and maybe the most straightforward
problem for the human brain because as a child we have been trained to classify
images, detect objects, but for computers, it can be a daunting task. Giving intuition
and reasoning to a computer which has mere capabilities to accept commands
and process those commands is a big challenge. However, recent leaps in hardware
development, sophisticated software frameworks, and mathematical techniques have
made it a little less daunting if not easy. There are various applications built around
to the concept of “Perception”. These applications require substantial computational
resources, expensive hardware, and some sophisticated software frameworks. Building
an application for perception for the embedded system is an entirely different
ballgame. Embedded system is a culmination of hardware, software and peripherals
developed for specific tasks with imposed constraints on memory and power.
Therefore, the applications developed should keep in mind the memory and power
constraints imposed due to the nature of these systems. Before 2012, the problems related to “Perception” such as classification, object
detection were solved using algorithms with manually engineered features. However,
in recent years, instead of manually engineering the features, these features are learned
through learning algorithms. The game-changing architecture of Convolution Neural
Networks proposed in 2012 by Alex K [1], provided a tremendous momentum in the
direction of pushing Neural networks for perception. This thesis is an attempt to
develop a convolution neural network architecture for embedded systems, i.e. an architecture that has a small model size and competitive accuracy. Recreate state-of-the-art architectures using fire module’s concept to reduce the model size of the
architecture. The proposed compact models are feasible for deployment on embedded
devices such as the Bluebox 2.0. Furthermore, attempts are made to integrate the
compact Convolution Neural Network with object detection pipelines.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/17921 |
| Date | 12 1900 |
| Creators | Pathak, Durvesh |
| Contributors | El-Sharkawy, Mohamed, Rizkalla, Maher, King, Brian |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Detected Language | English |
| Type | Thesis |
| Rights | Attribution-NonCommercial 3.0 United States, http://creativecommons.org/licenses/by-nc/3.0/us/ |
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