Pipeline Defect Detection Based on Improved YOLOv11

Underground utility tunnels face corrosion, cracks, and leakage after long-term use, endangering urban safety. Traditional methods have strong subjectivity, high miss rates, and poor real-time performance, failing refined management needs. This paper proposes an attention-enhanced YOLOv11 rather than YOLOv10 because its C3k2 backbone and dynamic anchor head already surpass YOLOv10 by 1.8% mAP for pipeline defect detection in utility tunnels. It uses homomorphic filtering to improve low-light image quality; replaces the last two C3k2 modules of the original YOLOv11 with a Multi-Scale Feature Aggregation Module to capture micro-cracks via expanded receptive fields; introduces a bidirectional weighted feature pyramid network in the neck (with C2PSA/BRA attention) for cross-scale feature fusion and background suppression, which yields both fine-grained micro-crack sensitivity and global false-target suppression; and adopts DIoU loss in the detection head to reduce slender defect localization errors. Experiments on 5000 utility tunnel defect images show the improved algorithm achieves 93.2% precision, 92.4% recall, and 92.6% mAP—outperforming the original YOLOv11, Faster R-CNN, and YOLOv5. Ablation experiments confirm module effectiveness, cutting relative error by 75% compared with the baseline. This algorithm can accurately identify multiple types of defects in complex utility tunnel environments, providing technical support for the safe and efficient operation and maintenance of urban infrastructure.