Citation: Iqbal, U.; Riaz, M.Z.B.; Barthelemy, J.; Hutchison, N.; Perez, P. Floodborne Objects Type Recognition Using Computer Vision to Mitigate Blockage Originated Floods. Water 2022, 14, 2605. https:// doi.org/10.3390/w14172605 Academic Editor: Fi-John Chang Received: 23 July 2022 Accepted: 19 August 2022 Published: 24 August 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). water Article Floodborne Objects Type Recognition Using Computer Vision to Mitigate Blockage Originated Floods Umair Iqbal 1, * , Muhammad Zain Bin Riaz 2 , Johan Barthelemy 3 , Nathanael Hutchison 1 and Pascal Perez 1 1 SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW 2522, Australia 2 School of Civil, Mining and Environmental Engineering, University of Wollongong, Wollongong, NSW 2522, Australia 3 NVIDIA Incorporation Ltd., Santa Clara, CA 95051, USA * Correspondence: umair@uow.edu.au Abstract: The presence of floodborne objects (i.e., vegetation, urban objects) during floods is con- sidered a very critical factor because of their non-linear complex hydrodynamics and impacts on flooding outcomes (e.g., diversion of flows, damage to structures, downstream scouring, failure of structures). Conventional flood models are unable to incorporate the impact of floodborne objects mainly because of the highly complex hydrodynamics and non-linear nature associated with their kinematics and accumulation. Vegetation (i.e., logs, branches, shrubs, entangled grass) and urban objects (i.e., vehicles, bins, shopping carts, building waste materials) offer significant materialistic, hy- drodynamic and characterization differences which impact flooding outcomes differently. Therefore, recognition of the types of floodborne objects is considered a key aspect in the process of assessing their impact on flooding. The identification of floodborne object types is performed manually by the flood management officials, and there exists no automated solution in this regard. This paper proposes the use of computer vision technologies for automated floodborne objects type identification from a vision sensor. The proposed approach is to use computer vision object detection (i.e., Faster R-CNN, YOLOv4) models to detect a floodborne object’s type from a given image. The dataset used for this research is referred to as the “Floodborne Objects Recognition Dataset (FORD)” and includes real images of floodborne objects blocking the hydraulic structures extracted from Wollongong City Council (WCC) records and simulated images of scaled floodborne objects blocking the culverts collected from hydraulics laboratory experiments. From the results, the Faster R-CNN model with MobileNet backbone was able to achieve the best Mean Average Precision (mAP) of 84% over the test dataset. To demonstrate the practical use of the proposed approach, two potential use cases for the proposed floodborne object type recognition are reported. Overall, the performance of the implemented computer vision models indicated that such models have the potential to be used for automated identification of floodborne object types. Keywords: blockage of hydraulic structures; computer vision; object detection; floodborne objects; floods 1. Introduction Floods are one of the natural disasters which usually occur on a large scale and result in catastrophic damage to the community [1,2]. Rapid evacuations, damage to property, wildlife loss, human causalities and agricultural damage are a few of the most highlighted damages from a flooding event [3,4]. The frequency of rain-originated floods has been ob- served to be increasing over the last couple of decades mainly because of an increase in the duration and intensity of rainfalls and blockage of urban drainage structures. The rain is a naturally occurring phenomenon and hence cannot be controlled; however, drainage struc- tures (e.g., bridges, culverts, sewerage) can be efficiently managed to avoid urban floods to a significant extent. Hydraulic structures including bridges and culverts are vulnerable to Water 2022, 14, 2605. https://doi.org/10.3390/w14172605 https://www.mdpi.com/journal/water