A Deep Learning Benchmark Analysis of the Publicly Available WRc Dataset for Sewer Defect Classification

<p dir="ltr">Deep learning has the potential to transform sewer pipe inspection by automating the process, which could improve efficiency and consistency. However, progress has been hampered by limited publicly available, well- annotated benchmark datasets for defect classification. To address this gap, we present a comprehensive analysis using the publicly available Water Research Centre (WRc) sewer image dataset. We evaluated several deep learning architectures (MobileNet-v2, Inception-ResNet-v2 and ResNet-18) across key performance metrics such as accuracy and F1-score, with Top-1 accuracies ranging from 61.54% to 71.61% and Top-3 accuracies ranging from 86.88% to 92.61%. This research contributes to a reproducible performance baseline, enabling rigorous comparison of different models and serves as a foundation for future research in developing AI-assisted inspection systems.</p><p dir="ltr">This paper was presented at the 21st Computing and Control in the Water Industry Conference (CCWI 2025) at the University of Sheffield (1st - 3rd September 2025).</p><p dir="ltr"><br></p>