An Integrated CNN, YOLOv5 and Faster R-CNN Framework for Real-Time Water Pipe Defect Detection

In the context of rapidly expanding urban water supply networks and the prevalence of pipe defects – for example, corrosion, cracks, leaks, blockages – that undermine efficiency and pose safety risks, this study presents an intelligent detection system aimed at improving maintenance accuracy and operational stability. We propose a fusion-based detection architecture combining Convolutional Neural Networks for stable multi‐level feature extraction, YOLOv5 for high‐speed real‐time detection, and Faster R‐CNN for enhanced recall of small or occluded defects. Individually, the models achieve 85.0% accuracy for the CNN extractor, 90.0% detection accuracy with 50 FPS for YOLOv5, and 86.8% recall for Faster R‐CNN. Ablation experiments confirm that the fully integrated system attains superior performance—92.1% accuracy, 85.0% recall, an F1 score of 81.0, and an mAP of 85.1 at 45 FPS—demonstrating that ensemble methods harness complementary strengths to optimize detection precision and speed. Overall, our findings highlight the promise of deep learning–based ensembles for large‐scale, real‐time pipeline inspection, offering a foundation for future intelligent infrastructure management.