GUANZihao,ZHANGZhihao,HEShuya,et al.Drainage Pipeline Defect Detection via YOLO and Feature Extraction Guided SAM Segmentation[J].China Water & Wastewater,2026,42(11):122-127.
Drainage Pipeline Defect Detection via YOLO and Feature Extraction Guided SAM Segmentation
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第42卷
Number:
第11期
Page:
122-127
Column:
Date of publication:
2026-06-01
- Keywords:
- pipeline defect detection; SAM model; phase consistency; semantic segmentation; defect grading
- Abstract:
- With the increasing complexity of urban drainage systems, traditional manual inspection and single model-based defect detection methods suffer from low recognition efficiency, insufficient segmentation accuracy, and heavy reliance on large annotated datasets. To address these technical challenges, this study proposes an innovative defect detection method that integrates the YOLOv4 object detection algorithm with multi-feature extraction-guided SAM (segment anything model). The experiments demonstrated that, although the YOLO+SAM integrated approach achieved effective segmentation in conventional scenarios, its performance in handling complex defect shapes (such as cracks, root invasions, and disjoint) remained inadequate. To this end, three multimodal feature extraction methods—phase consistency analysis, fractal dimension matrix computation, and edge detection optimization—were introduced to enhance the model’s ability to represent heterogeneous defects. Experimental results showed that the combination of YOLO+feature extraction+SAM achieved significant improvements in detecting typical defects like cracks, root invasions, and disjoint compared to the YOLO+SAM approach: the recall rate increased by 17.1% to 24.3%, precision improved by 24.9% to 44.4%, and the F1 score increased by 23.1% to 32.4%. This method not only provides clearer defect segmentation boundaries and higher recognition accuracy but also significantly reduces model training costs and labeling requirements, demonstrating substantial engineering application value.
Last Update:
2026-06-01