  Blockage of culverts by transported debris materials is reported as main
contributor in originating urban flash floods. Conventional modelling
approaches had no success in addressing the problem largely because of
unavailability of peak floods hydraulic data and highly non-linear behaviour of
debris at culvert. This article explores a new dimension to investigate the
issue by proposing the use of Intelligent Video Analytic (IVA) algorithms for
extracting blockage related information. Potential of using existing
Convolutional Neural Network (CNN) algorithms (i.e., DarkNet53, DenseNet121,
InceptionResNetV2, InceptionV3, MobileNet, ResNet50, VGG16, EfficientNetB3,
NASNet) is investigated over a custom collected blockage dataset (i.e., Images
of Culvert Openings and Blockage (ICOB)) to predict the blockage in a given
image. Models were evaluated based on their performance on test dataset (i.e.,
accuracy, loss, precision, recall, F1-score, Jaccard-Index), Floating Point
Operations Per Second (FLOPs) and response times to process a single test
instance. From the results, NASNet was reported most efficient in classifying
the blockage with the accuracy of 85\%; however, EfficientNetB3 was recommended
for the hardware implementation because of its improved response time with
accuracy comparable to NASNet (i.e., 83\%). False Negative (FN) instances,
False Positive (FP) instances and CNN layers activation suggested that
background noise and oversimplified labelling criteria were two contributing
factors in degraded performance of existing CNN algorithms.
