Automating Visual Blockage Classification of Culverts with Deep Learning

TL;DR

This paper investigates the use of deep learning CNN models to automatically classify culvert blockages from images, aiming to improve urban flood management by providing a reliable detection method.

Contribution

It evaluates multiple CNN architectures on a custom blockage dataset and recommends EfficientNetB3 for practical hardware implementation due to its balance of accuracy and response time.

Findings

NASNet achieved 85% accuracy in blockage classification.

EfficientNetB3 provided comparable accuracy with faster response times.

Background noise and labeling issues affected CNN performance.

Abstract

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.