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Automated Defect Classification in Sewer Closed Circuit Television Inspections Using Deep Convolutional Neural Networks

Automated interpretation of sewer closed-circuit television (CCTV) inspection videos could improve the speed, accuracy, and consistency of sewer defect reporting. Previous research has attempted to use computer vision, namely feature extraction methods for automated classification of defects in sewer CCTV images. However, feature extraction methods use pre-engineered features for classifying images, leading to poor generalization capabilities. Due to large variations in sewer images arising from differing pipe diameters, in-situ conditions (e.g., fog and grease), etc., previous automated methods suffer from poor classification performance when applied to sewer CCTV videos. This paper presents a framework that uses an ensemble of binary deep convoluted neural networks (CNNs) to classify multiple defects in sewer CCTV images. A prototype system was developed to classify root intrusions, deposits, and cracks. The CNNs were trained and tested using 12,000 images collected from over 200 pipelines. The average testing accuracy, precision and recall were 86.2%, 87.7% and 90.6%, respectively, demonstrating the viability of this approach in the automated interpretation of sewer CCTV videos.

Related Publication

Srinath S. Kumara, Dulcy M. Abraham, Mohammad R. Jahanshahi, Tom Iseley and Justin Starrc, (2018), "Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks," Automation in Construction, accepted.