Xiao Cong, Zhou Yiyuan, Wu Yanwen, Zhang Guodong
School of Physical Sciences and Technology, Central China Normal University, Wuhan, China.
School of Computer Science and Technology, Hankou University, Wuhan, China.
PLoS One. 2025 Jan 16;20(1):e0316951. doi: 10.1371/journal.pone.0316951. eCollection 2025.
The prevailing trend in industrial equipment development is integration, with pipelines as the lifeline connecting system components. Given the often harsh conditions of these industrial equipment pipelines, leakage is a common occurrence that can disrupt normal operations and, in severe cases, lead to safety accidents. Early detection of even minor drips at the onset of leakage can enable timely maintenance measures, preventing more significant leaks and halting the escalation of pipeline failures. In light of this, our study investigates a method for monitoring pipe drips in industrial equipment using machine vision technology. We propose a machine vision model specifically designed for pipe drip detection, aiming to facilitate monitoring of pipe system drips. The system designed to collect the image of the droplet side cross-section with a Charge charge-coupled device (CCD) industrial camera, is aided by the computer image processing system used to analyze and process the collected images. Image enhancement technology is applied to improve the visibility of the image and image filtering technology is applied to remove the noise of the image. With the help of image segmentation technology, target droplet identification and division are achieved. Morphological reconstruction and region-filling techniques are used to remove the noise caused by shooting in the side cross-section image, such as hollow, reflection, and irregular droplet edge, to upgrade the quality of the solution droplet edge. The mathematical model is established for boundary position points extracted from the droplet side cross-section image. Then, the fitting droplet image is drawn. The droplet volume is obtained by calculating the volume of the rotating body. The two-dimensional image of the target droplet is obtained dynamically through the camera capture technology. The droplet boundary extraction algorithm is proposed, and the three-dimensional model of the target droplet is established, so the volume calculation problem of the droplet is solved, which provides a way of thinking for drip leakage detection of the pipeline.
工业设备发展的主流趋势是集成化，管道作为连接系统组件的生命线。鉴于这些工业设备管道的条件往往恶劣，泄漏是常见现象，可能扰乱正常运行，严重时会导致安全事故。在泄漏开始时即使检测到微小的滴漏，也能及时采取维护措施，防止更严重的泄漏并阻止管道故障升级。有鉴于此，我们的研究调查了一种利用机器视觉技术监测工业设备管道滴漏的方法。我们提出了一种专门为管道滴漏检测设计的机器视觉模型，旨在便于对管道系统滴漏进行监测。该系统旨在使用电荷耦合器件（CCD）工业相机采集液滴侧面横截面的图像，并借助计算机图像处理系统对采集到的图像进行分析和处理。应用图像增强技术来提高图像的清晰度，并应用图像滤波技术去除图像噪声。借助图像分割技术，实现目标液滴的识别和分割。使用形态学重建和区域填充技术去除侧面横截面图像中拍摄产生的噪声，如空洞、反射和不规则的液滴边缘，以提升液滴边缘的解决方案质量。针对从液滴侧面横截面图像中提取的边界位置点建立数学模型。然后，绘制拟合的液滴图像。通过计算旋转体的体积获得液滴体积。通过相机捕捉技术动态获取目标液滴的二维图像。提出了液滴边界提取算法，并建立了目标液滴的三维模型，从而解决了液滴的体积计算问题，为管道滴漏检测提供了一种思路。
