Classification of road side material using convolutional neural network and a proposed implementation of the network through Zedboard Zynq 7000 FPGA
type: CreativeWork
description: In recent years, Convolutional Neural Networks (CNNs) have become the state-of- the-art method for object detection and classi cation in the eld of machine learning and arti cial intelligence. In contrast to a fully connected network, each neuron of a convolutional layer of a CNN is connected to fewer selected neurons from the previous layers and kernels of a CNN share same weights and biases across the same input layer dimension. These features allow CNN architectures to have fewer parameters which in turn reduces calculation complexity and allows the network to be implemented in low power hardware. The accuracy of a CNN depends mostly on the number of images used to train the network, which requires a hundred thousand to a million images. Therefore, a reduced training alternative called transfer learning is used, which takes advantage of features from a pre-trained network and applies these features to the new problem of interest. This research has successfully developed a new CNN based on the pre-trained CIFAR-10 network and has used transfer learning on a new problem to classify road edges. Two network sizes were tested: 32 and 16 Neuron inputs with 239 labeled Google street view images on a single CPU. The result of the training gives 52.8% and 35.2% accuracy respectively for 250 test images. In the second part of the research, High Level Synthesis (HLS) hardware model of the network with 16 Neuron inputs is created for the Zynq 7000 FPGA. The resulting circuit has 34% average FPGA utilization and 2.47 Watt power consumption. Recommendations to improve the classi cation accuracy with deeper network and ways to t the improved network on the FPGA are also mentioned at the end of the work.
abstract: In recent years, Convolutional Neural Networks (CNNs) have become the state-of- the-art method for object detection and classi cation in the eld of machine learning and arti cial intelligence. In contrast to a fully connected network, each neuron of a convolutional layer of a CNN is connected to fewer selected neurons from the previous layers and kernels of a CNN share same weights and biases across the same input layer dimension. These features allow CNN architectures to have fewer parameters which in turn reduces calculation complexity and allows the network to be implemented in low power hardware. The accuracy of a CNN depends mostly on the number of images used to train the network, which requires a hundred thousand to a million images. Therefore, a reduced training alternative called transfer learning is used, which takes advantage of features from a pre-trained network and applies these features to the new problem of interest. This research has successfully developed a new CNN based on the pre-trained CIFAR-10 network and has used transfer learning on a new problem to classify road edges. Two network sizes were tested: 32 and 16 Neuron inputs with 239 labeled Google street view images on a single CPU. The result of the training gives 52.8% and 35.2% accuracy respectively for 250 test images. In the second part of the research, High Level Synthesis (HLS) hardware model of the network with 16 Neuron inputs is created for the Zynq 7000 FPGA. The resulting circuit has 34% average FPGA utilization and 2.47 Watt power consumption. Recommendations to improve the classi cation accuracy with deeper network and ways to t the improved network on the FPGA are also mentioned at the end of the work.
author: Rahman, Tanvir
language: en_US
keywords: Convolutional Neural Net, Field Programmable Gate Array, Transfer Learninig, Self Organizing Maps, High Level Synthesis, CIFAR-10, Classification, ZedBoard
license: http://creativecommons.org/licenses/by/3.0/us/
date published: 2017-12
date modified: 2026-07-24T02:42:13Z
provider: IUPUI
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: IUPUI

PropertyValue
property ID: Handle
value: https://hdl.handle.net/1805/15113

PropertyValue
property ID: DOI
value: 10.7912/C2/2450

PropertyValue
property ID: DOI
value: 10.7912/C24Q0X