Document Type

Conference Paper

Publication Date

2024

DOI

10.1117/12.3028126

Publication Title

Optics and Photonics for Information Processing XVIII

Volume

13136

Pages

1313603

Conference Name

Optical Engineering + Applications, 18-23 August 2024, San Diego, CA

Abstract

Recurrent nuisance flooding is common across many parts of the globe and causes extensive challenges for drivers on the roadways. The prevailing monitoring methods for roadway flooding are costly and not automated or effective. The ubiquity of visual data from cameras and advancements in computing such as deep learning may offer cost-effective methods for automated flood depth estimation on roadways based on reference objects such as cars. However, flood depth estimation faces challenges due to the limited amount of data annotated with water levels and diverse scenes showing reference objects at various scales and perspectives. This study proposes a novel deep learning approach to automated flood depth estimation on roadways. Our proposed pipeline addresses variations in object perspective and scale. We have developed an innovative approach to generate and annotate flood images by manipulating existing image datasets of cars in various orientations and scales to simulate four floodwater levels for augmenting real flood images. Furthermore, we propose object scale normalization for our reference objects (cars) to improve water level predictions. The proposed model achieves an accuracy of 74.85% and F1 score of 74.32% for four water levels when tested with real flood data. The proposed approach substantially reduces the time and labor required for labeling datasets while addressing challenges in perspective/scale, offering a promising solution for image-based flood depth estimation.

Rights

Copyright 2024 Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.

ORCID

0000-0002-6578-4657 (Witherow), 0000-0003-1794-916X (Rahman), 0000-0003-2003-9343 (Cetin), 0000-0001-8316-4163 (Iftekharuddin)

Original Publication Citation

Ampofo, K., Witherow, M., Glandon, A., Rahman, M., Temtam, A., Cetin, M., & Iftekharuddin, K. M. Automated flood depth estimation on roadways, in Optics and Photonics for Information Processing XVIII, edited by Khan M. Iftekharuddin, Abdul A. S. Awwal, Victor Hugo Diaz-Ramirez, Andrés Márquez, Proc. of SPIE 13136, 1313603 [30/09/2024]; https://doi.org/10.1117/12.3028126

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