Published Conf paper ATDE-15-ATDE210005.pdf (953.01 kB)
Development of an optimised dataset for training a deep neural network
conference contributionposted on 2021-11-15, 10:23 authored by Callum 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.
Loughborough University NPIF 2018
Engineering and Physical Sciences Research CouncilFind out more...
- Mechanical, Electrical and Manufacturing Engineering
Published inAdvances in Manufacturing Technology
Pages15 - 20
Source18th International Conference on Manufacturing Research, incorporating the 35th National Conference on Manufacturing Research
- VoR (Version of Record)
Rights holder© The authors and IOS Press
Publisher statementThis 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/
Book seriesAdvances in Transdisciplinary Engineering; 15