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Development of an optimised dataset for training a deep neural network

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conference contribution
posted on 15.11.2021, 10:23 authored by Callum NewmanCallum Newman, Jon PetzingJon Petzing, Yee GohYee Goh, Laura JusthamLaura Justham
Artificial intelligence in computer vision has focused on improving test performance using techniques and architectures related to deep neural networks. However, improvements can also be achieved by carefully selecting the training dataset images. Environmental factors, such as light intensity, affect the image's appearance and by choosing optimal factor levels the neural network's performance can improve. However, little research into processes which help identify optimal levels is available. This research presents a case study which uses a process for developing an optimised dataset for training an object detection neural network. Images are gathered under controlled conditions using multiple factors to construct various training datasets. Each dataset is used to train the same neural network and the test performance compared to identify the optimal factors. The opportunity to use synthetic images is introduced, which has many advantages including creating images when real-world images are unavailable, and more easily controlled factors.

Funding

Loughborough University NPIF 2018

Engineering and Physical Sciences Research Council

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History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Advances in Manufacturing Technology

Volume

14

Pages

15 - 20

Source

18th International Conference on Manufacturing Research, incorporating the 35th National Conference on Manufacturing Research

Publisher

IOS Press

Version

VoR (Version of Record)

Rights holder

© The authors and IOS Press

Publisher statement

This is an Open Access Article. It is published by IOS Press under the Creative Commons Attribution-NonCommercial 4.0 International Licence (CC BY-NC 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by-nc/4.0/

Publication date

2021-08-21

Copyright date

2021

ISBN

9781643681986; 9781643681993

Book series

Advances in Transdisciplinary Engineering; 15

Language

en

Editor(s)

Mahmoud Shafik; Keith Case

Location

University of Derby, Derby, UK

Event dates

7-10 September 2021

Depositor

Dr Jon Petzing. Deposit date: 12 November 2021

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