Analysis of Yolov11 Model for Real-World Pipeline Defect Detection

Abstract

With the rapid urbanization, the scale of underground pipeline systems continues to expand, making the detection and maintenance of pipeline defects increasingly critical. Traditional pipeline defect detection methods primarily rely on manual inspections and mechanical means, which are inefficient and prone to false positives and missed detections. To address these issues, this paper proposes an automatic pipeline defect detection method based on the YOLOv11 object detection algorithm. By constructing a diversified dataset and leveraging YOLOv11's deep convolutional neural network, the model efficiently identifies defects such as cracks, corrosion, and misalignment in pipelines. Experimental results show that YOLOv11 outperforms traditional manual methods in both detection accuracy and speed. In particular, YOLOv11 demonstrates strong detection capabilities for small objects and complex backgrounds, significantly improving detection efficiency. Compared to manual inspection, YOLOv11 reduces detection time by over 30% and exhibits a low miss detection rate. In the future, the application of YOLOv11 in pipeline detection will be further extended to real-time detection and multi-modal data fusion, enhancing its adaptability in complex environments.