PIPE-CovNet+: A Hyper-Dense CNN for Improved Pipe Abnormality Detection

Abstract

In this letter, we propose a PIPE-CovNet+ model that is ba-sed on convolutional neural networks (CNNs) with multiple kernel sizes, gradient boosting techniques, and hyper-densely connected layers for abnormality detection in wastewater pipe infrastructure. The PIPE-CovNet+ model achieved 85% accuracy and 84% F1-score following assessment utilizing an open-source dataset, overcoming the constraints of imbalanced data and over-fitting issues. Furthermore, it showed greater efficacy when compared against other relevant models, displaying 3% higher accuracy and F1-score than our prior work under ideal test settings. Installing PIPE-CovNet+ into a robot system can alleviate the limitations of being tedious, laborious, and prone to human error in closed-circuit television (CCTV) inspections.