Quantification of visual blockage at culverts using deep learning based computer vision models

ABSTRACT

Blockage of culverts by debris material is reported as main cause of urban flash floods. Extraction of blockage information using intelligent video analytic (IVA) algorithms can prove helpful in making timely maintenance-related decisions toward avoiding flash floods. Having known the percentage of visual blockage at culverts can help better prioritise the maintenance of highly blocked culvert sites. This article proposes a deep learning-based segmentation-classification pipeline where visible culvert openings are segmented at the first stage and classified into one of four percentage visual blockage classes at the second stage. Images of Culverts and Blockage (ICOB) and Visual Hydraulics-Lab Blockage Dataset (VHD) dataset were used to train the deep learning models. From the results, Mask R-CNN (ResNet50 backbone) achieved the best segmentation performance (i.e. mAP@75 of 77.2%), while NASNet achieved the best classification performance (i.e. 81.2% test accuracy). To demonstrate the implication, a potential visual blockage monitoring use-case has been proposed.

KEYWORDS:

• Visual blockage
• culverts
• deep learning pipeline
• computer vision
• Convolutional Neural Networks (CNNs)
• Intelligent Video Analytic (IVA)

Acknowledgements

I would like to thank the Higher Education Commission (HEC) of Pakistan and the University of Wollongong (UOW) for funding my PhD studies. Thanks to Associate Professor Wanqing Li for his technical assistance in the process.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.