An Effective Low-Contrast SF₆ Gas Leakage Detection Method for Infrared Imaging

• Published in: IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 9
• Main Authors: Lu, Quan, Li, Qin, Hu, Likun, Huang, Lifeng
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Background noise; Dark adaptation; Detection algorithms; Dynamic interframe filtering algorithm; Equalization; Filtering; Filtering algorithms; Histograms; Image contrast; Image enhancement; image motion analysis; Image segmentation; Infrared imagery; Infrared imaging; infrared imaging enhancement; Leak detection; low-contrast detection; moving detection; Noise; Probabilistic models; Random noise; SF₆ gas detection; Sulfur hexafluoride; Time-domain analysis
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2021.3073443

Infrared noncontact detection has the advantages of having no power cut-off and convenient operation. However, when the SF 6 (sulfur hexafluoride) gas leakage is small, the existing detection algorithms have low accuracy and are easily disturbed by noise. To solve this problem, this article proposes an online detection method for SF 6 gas leakage based on a Gaussian mixture model. First, as a preprocessing stage, the improved dynamic interframe time domain filter is used to suppress the random noise in the image. Then, contrast limited adaptive histogram equalization (CLAHE) is used to enhance the dark details of the infrared image to improve the local contrast. Finally, the improved Gaussian mixture background model is used to adaptively segment the SF 6 gas leakage and mark the leakage area. The results show that under the experimental conditions of 0.06 mL/min indoor and 5-m distance, the algorithm can overcome the high noise and complex background disturbances of infrared imaging. It can effectively detect and locate the SF 6 leakage area and gets a higher F1-score compared with other conventional algorithm under the experimental conditions. In addition, when the resolution of the infrared image is <inline-formula> <tex-math notation="LaTeX">320 \times 240 </tex-math></inline-formula>, the running efficiency of the algorithm meets the real-time requirements.