Enhanced Detection of Narrow-Band Leakage Gas Infrared Image Based on Bilateral Filtering and U-Net

Uncooled infrared image detection methods with a narrow-band filter have the advantages of low cost and can efficiently distinguish gas types; however, they also introduce nonuniform patterns to the image and reduce the target radiation flux and signal-to-noise ratio (SNR), resulting in low gas extraction accuracy. In this study, first, a temperature control device is designed in a part containing a filter and a detector array to effectively suppress dark current noise, which is positively correlated with temperature and eliminates nonuniform patterns, such as the lid effect, using the two-point correction method. Second, according to the infrared image characteristics of the leaked gas, a leakage gas infrared image enhancement algorithm based on bilateral filtering is developed. This algorithm considerably reduces the time frame noise of image sequences, improves the contrast between the gas plume region and background, and improves the SNR. Next, based on an improved fuzzy Gaussian background model, U-Net is used to separate the gas and non-gas foreground. Experimental results demonstrate that the proposed method exhibits high gas plume detection accuracy.