Pipeline leakage can lead to catastrophic consequences, and traditional sensor-based detection methods often struggle to identify changes caused by slow or minor leaks. This paper proposes a real-time machine vision-based method for detecting liquid leakage in pipelines, suitable for complex industrial scenarios. By extracting droplet foreground regions and constructing a detection model based on the contour and motion features of droplets, the proposed method effectively filters out interference from lighting variations, equipment vibrations, and personnel movement in industrial environments, while accurately identifying the vertical motion characteristics of dripping liquids. An experimental platform was established to validate the effectiveness of the proposed approach. The results demonstrate that the proposed method achieves a detection rate of 98.04%, a false alarm rate of 5.26%, and a processing speed of 90.71 fps. Comparative experiments show that this method significantly outperforms traditional approaches, such as the dense optical flow method, which yields a higher false alarm rate and a processing speed of only 2.2 fps under the same test conditions. These findings confirm that our approach offers a more accurate and efficient solution for real-time pipeline liquid leakage detection.
Zeng et al. (Sun,) studied this question.