Application of YOLOv5 object detection in pipeline radar imaging

In the forefront of contemporary urban infrastructure management and maintenance, monitoring the health status of pipeline systems is of paramount importance. However, conventional approaches to Ground Penetrating Radar (GPR) image data analysis heavily rely on the subjective judgment of expert personnel, making the process both time-consuming and prone to individual biases, thereby limiting its potential for large-scale application. This paper sets out to explore the innovative application of the YOLOv5 model in pipeline radar imaging, harnessing the power of deep learning algorithms to enable rapid and accurate identification of critical information such as underground pipeline defects and foreign object intrusions. Ultimately, the detection algorithm of this model achieves precision and recall rates of 91.8% and 89.3%, respectively, in identifying underground pipelines. These metrics not only meet but also exceed the practical requirements for engineering applications, highlighting the efficacy and robustness of the proposed YOLOv5-based approach in enhancing the efficiency and accuracy of pipeline inspection tasks.