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

Abstract

Infrared noncontact detection has the advantages of having no power cut-off and convenient operation. However, when the SF6 (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 SF6 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 SF6 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 SF6 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 $320 \times 240$ , the running efficiency of the algorithm meets the real-time requirements.