Liquid leakage from pipelines is a critical issue in large-scale chemical process plants since it can affect the normal operation of the plant and pose unsafe and hazardous situations. Therefore, leakage detection in the early stages can prevent serious damage. Developing a vision-based inspection system by means of IR imaging can be a promising approach for accurate leakage detection. IR cameras can capture the effect of leaking drops if they have higher (or lower) temperature than their surroundings. Since the leaking drops can be observed in an IR video as a repetitive phenomenon with specific patterns, motion pattern detection methods can be utilized for leakage detection. In this paper, an approach based on the Kalman filter is proposed to track the motion of leaking drops and differentiate them from noise. The motion patterns are learned from the training data and applied to the test data to evaluate the accuracy of the method. For this purpose, a laboratory demonstrator plant is assembled to simulate the leakages from pipelines, and to generate training and test videos. The results show that the proposed method can detect the leaking drops by tracking them based on obtained motion patterns. Furthermore, the possibilities and conditions for applying the proposed method in a real industrial chemical plant are discussed at the end.

中文翻译：

---

使用热成像视频和运动模式检测进行化学加工厂的常规泄漏检查

从管道泄漏的液体在大型化学过程工厂中是一个关键问题，因为它会影响工厂的正常运行并造成不安全和危险的情况。因此，早期的泄漏检测可以防止严重的损坏。通过红外成像开发基于视觉的检查系统可能是用于准确泄漏检测的有前途的方法。如果红外摄像头的温度高于（或低于）周围环境，则红外摄像头可以捕获滴漏的影响。由于可以在IR视频中观察到泄漏滴，这是具有特定图案的重复现象，因此可以将运动图案检测方法用于泄漏检测。在本文中，提出了一种基于卡尔曼滤波器的方法来跟踪泄漏液滴的运动并将其与噪声区分开。从训练数据中学习运动模式，并将其应用于测试数据以评估该方法的准确性。为此，组装了一个实验室演示工厂来模拟管道泄漏，并生成培训和测试视频。结果表明，所提出的方法可以基于获得的运动模式通过跟踪泄漏点来检测泄漏点。此外，最后讨论了在实际的工业化工厂中采用该方法的可能性和条件。结果表明，所提出的方法可以基于获得的运动模式通过跟踪泄漏点来检测泄漏点。此外，最后讨论了将建议的方法应用于实际的工业化工厂的可能性和条件。结果表明，所提出的方法可以基于获得的运动模式通过跟踪泄漏点来检测泄漏点。此外，最后讨论了在实际的工业化工厂中采用该方法的可能性和条件。。