Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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Preliminary Study on Shadow Detection in Drone-Acquired Images with U-NET

Authors
Siti-Aisyah Zali1, Shahbe M-Desa1, *, Zarina Che-Embi1, Wan-Noorshahida Mohd-Isa1
1Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia
*Corresponding author. Email: shahbe@mmu.edu.my
Corresponding Author
Shahbe M-Desa
Available Online 27 December 2022.
DOI
10.2991/978-94-6463-094-7_28How to use a DOI?
Keywords
Shadow detection; Deep learning; Aerial images; Data augmentation
Abstract

This study shows a preliminary investigation of shadow detection in drone-acquired images using a deep learning method with minimal labelled shadow images. The aim is to discuss how the selected U-Net architecture performs in a small-sized dataset consisting of various types of shadow brightness and objects. Two types of data augmentation methods, which are shadow variant and geometric transformation are implemented, aiming to improve the segmentation accuracy. Several experimental procedures are performed to observe the model performance. The study shows that adding images for training increases the accuracy of shadow detection in drone images from 0.95 to 0.96, and geometric transformation data augmentation method increases the accuracy from 0.961 to 0.963, while the shadow variant method increases the flexibility of detection.

Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
Series
Atlantis Highlights in Computer Sciences
Publication Date
27 December 2022
ISBN
10.2991/978-94-6463-094-7_28
ISSN
2589-4900
DOI
10.2991/978-94-6463-094-7_28How to use a DOI?
Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Siti-Aisyah Zali
AU  - Shahbe M-Desa
AU  - Zarina Che-Embi
AU  - Wan-Noorshahida Mohd-Isa
PY  - 2022
DA  - 2022/12/27
TI  - Preliminary Study on Shadow Detection in Drone-Acquired Images with U-NET
BT  - Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
PB  - Atlantis Press
SP  - 357
EP  - 368
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-094-7_28
DO  - 10.2991/978-94-6463-094-7_28
ID  - Zali2022
ER  -