Deep learning for automated trash screen blockage detection using cameras: actionable information for flood risk management

Vandaele, R. ORCID: https://orcid.org/0000-0002-0693-8011, Dance, S. L. ORCID: https://orcid.org/0000-0003-1690-3338 and Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192 (2024) Deep learning for automated trash screen blockage detection using cameras: actionable information for flood risk management. Journal of Hydroinformatics, 26 (4). pp. 889-903. ISSN 1465-1734 doi: 10.2166/hydro.2024.013

Abstract/Summary

Trash screens are used to prevent debris from entering critical parts of rivers. However, debris can accumulate on the screen and generate floods. This makes their monitoring critical both for maintenance and flood modeling purposes (e.g., local forecasts may change because the trash screen is blocked). We developed three novel deep learning methods for trash screen maintenance management consisting of automatically detecting trash screen blockage using cameras: a method based on image classification, a method based on image similarity matching, and a method based on anomaly detection. In order to facilitate their use by end users, these methods are designed so that they can be directly applied to any new trash screen camera installed by the end users. We have built a new dataset of labeled trash screen images to train and evaluate the efficiency of our methods, both in terms of accuracy and implications for end users. This dataset consists of 80,452 trash screen images from 54 cameras installed by the Environment Agency (UK). This work demonstrates that trash screen blockage detection can be automated using trash screen cameras and deep learning, which could have an impact on both trash screen management and flood modeling.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/115972
Identification Number/DOI	10.2166/hydro.2024.013
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	IWA Publishing
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