Advancing Water Infrastructure Maintenance: Integration of CNN, YOLOv5, and Faster R-CNN for Accurate Water Pipe Defect Localization and Classification

The increasing demands on urban water infrastructure have made efficient and accurate detection of defects in water pipe networks critical for ensuring operational reliability and resource management. This study presents a novel approach integrating Convolutional Neural Network (CNN), YOLOv5, and Faster R-CNN models to localize and classify water pipe defects. By leveraging a curated dataset of water pipe images, the CNN model demonstrated strong feature extraction capabilities, achieving an accuracy of 89.4%. YOLOv5 balanced detection accuracy (90.1%) and real-time processing (30 images per second), while Faster R-CNN excelled in defect localization with 87.1% accuracy. Compared to traditional edge detection and morphological methods, the proposed system significantly improves robustness, accuracy, and real-time applicability. The findings underscore the potential of deep learning in automating water pipe maintenance, contributing to smarter infrastructure management.