Multizone leak detection method for metal hose based on YOLOv5 and OMD-ViBe algorithm

Abstract

It is necessary to determine the location and number of leaks in a pipeline in time to repair it, thus reducing economic losses. A multizone leakage detection method based on the YOLOv5 and OMD-ViBe algorithm is proposed to detect the metal hose’s location and leakage rate. The deep learning model of YOLOv5 is used to accurately recognize the zone of the metal hose for the region of interest rectification. The multiframe averaging method is applied to construct the initial background of the video frames. The OTSU algorithm based on the background difference method and the adaptive threshold of the maximum intraclass and interclass variance ratio method is used to improve the recognition rate of bubbles and reduce the influence of illumination change. In a comparison with the existing algorithms, the experimental results showed that OMD-ViBe improves the F-measure by 1.79–16.41% and the percentage of misclassification by 0.003–0.165%. Analysis of the pressure data indicated a comprehensive leakage error reduction of 1.53–25.19%, which can meet the requirements of metal hose leakage detection.