Gas leakage detection based on spatiotemporal information of low contrast infrared images

Jinhui ZUO; Wenbin XU; Shijie ZHOU; Daobin SHENG; Xiangdong XU; Zhengqiang LI; Yinghui HAN; Chunjiang WU; Lei ZHANG

doi:10.37188/OPE.20243208.1186

Optics and Precision Engineering, Volume. 32, Issue 8, 1186(2024)

Gas leakage detection based on spatiotemporal information of low contrast infrared images

Jinhui ZUO1...2, Wenbin XU1,3, Shijie ZHOU4, Daobin SHENG5, Xiangdong XU7, Zhengqiang LI1,*, Yinghui HAN6, Chunjiang WU4 and Lei ZHANG5 |Show fewer author(s)

Author Affiliations

¹State Environmental Protection Key Laboratory of Satellite Remote Sensing & State Key Laboratory of Remote Sensing Science， Aerospace Information Research Institute， Chinese Academy of Sciences， Beijing000， China

²University of Chinese Academy of Sciences， Beijing100049， China

³Science and Technology on Optical Radiation Laboratory， Beijing Institute of Environmental Characteristics， Beijing100854， China

⁴School of Information and Software Engineering， University of Electronic Science and Technology ， Chengdu610000， China

⁵Jiangsu Ancline Technology Co， Nantong226000， China

⁶College of Resources and Environment， University of Chinese Academy of Sciences， Beijing100049， China

⁷Sichuan Yifang Intelligent Technology Co， Chengdu610054， China

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Figures & Tables(10)

Fig. 1. Uncooled long-wave infrared imaging system

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Fig. 2. Infrared absorption spectra of Butane

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Fig. 3. Experimental diagram （Case2）

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Fig. 4. Filter results for different methods

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Fig. 5. Results of different gas leak detection algorithms

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Fig. 6. Comparison of Invalid probability and Error under different algorithms

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Table 1. Basic process of the proposed algorithm

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Table 1. Basic process of the proposed algorithm

算法1：基于低对比度红外图像时空信息的气体泄漏检测

Input： $f_{t} (x, y)$

${\bar{f}}_{t} (x, y)$ ←Time-domain adaptive inter frame filtering $f_{t} (x, y)$

if t<N

spatiotemporal GMM update （ $f_{t_n e w}, α = 0.01 * e^{1 / N}$ ）

else：

spatiotemporal GMM update （ $f_{t_n e w}, α_{i, t} = 0.01 * m a t c h (f_{i, t})$ ）

end if

$F_{t} (x, y)$ ←spatiotemporal GMM （ ${\bar{f}}_{t} (x, y)$ ）

$F_{F R F C M & G C}^{t} (x, y)$ ←FRFCM&GC （ $F_{t} (x, y)$ ）

Output： $F_{F R F C M & G C}^{t} (x, y)$

Table 2. Comparison of filtering effects of different methods on HMDB51 dataset

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Table 2. Comparison of filtering effects of different methods on HMDB51 dataset

Method	Golf （0，0.1）		Walk （0，0.1）		Golf （0，0.05）		Walk （0，0.05）
Method	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM
Anisotropic filtering	22.301	0.478	21.080	0.479	26.829	0.5297	25.315	0.5487
Bilateral filtering	21.921	0.488	20.480	0.523	26.167	0.594	24.777	0.6194
Inter-frame filtering	21.989	0.607	21.656	0.732	26.402	0.664	27.021	0.825
Proposed filtering	23.580	0.696	23.420	0.745	28.612	0.7846	29.056	0.834

Table 3. Comparison of filtering effects of different methods on measured data

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Table 3. Comparison of filtering effects of different methods on measured data

Method	Case1		Case2		Case3		Case4
Method	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM
Anisotropic filtering	44.219	0.968	49.777	0.917	46.368	0.909	48.406	0.919
Bilateral filtering	39.083	0.958	41.855	0.925	42.474	0.929	40.137	0.919
Inter-frame filtering	48.875	0.989	55.635	0.993	44.456	0.998	57.4971	0.981
Proposed filtering	51.428	0.991	57.624	0.997	49.741	0.996	59.029	0.993

Table 4. Comparison of detection performance of different algorithms

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Table 4. Comparison of detection performance of different algorithms

Case	Case 1			Case 2			Case 3			Case 4
Case	Acc	Ip	Err	Acc	Ip	Err	Acc	Ip	Err	Acc	Ip	Err
FD	81.9	10.3	7.8	86.8	8.7	4.5	84.6	8.3	7.1	81.4	7.8	10.8
GMM	85.7	6.8	7.5	91.1	5.6	3.3	89.6	5.2	5.2	88.2	5.6	6.2
ViBe	84.8	8.4	6.8	90.2	6.9	2.9	89	5.6	5.4	85.6	6.8	7.6
LBAdaptive	90.2	4.7	5.1	94.8	2.6	2.6	93.6	3.3	3.1	90.6	4.6	4.8
Proposed	93.1	3.1	3.8	96.3	2.3	1.4	95.2	2.2	2.6	92.3	2.5	5.2
Case	Case 5			Case 6			Case 7			Case 8
Case	Acc	Ip	Err	Acc	Ip	Err	Acc	Ip	Err	Acc	Ip	Err
FD	84.4	7.1	8.5	81.7	8.6	9.7	83.7	9.1	7.2	91.8	3.7	4.5
GMM	88.1	5.3	6.6	87.6	6.9	5.5	90.2	4.7	5.1	94.2	2.6	3.2
ViBe	89.8	5.6	4.6	88.5	6.3	5.2	87.6	6.8	5.6	94.6	3.1	2.3
LBAdaptive	92.6	2.2	5.2	90.6	4.2	5.2	93.4	2.6	4	95.4	1.9	2.7
Proposed	93.2	2.6	4.2	92.3	2.9	4.8	93.6	3.2	3.2	94.8	1.6	3.6

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Jinhui ZUO, Wenbin XU, Shijie ZHOU, Daobin SHENG, Xiangdong XU, Zhengqiang LI, Yinghui HAN, Chunjiang WU, Lei ZHANG. Gas leakage detection based on spatiotemporal information of low contrast infrared images[J]. Optics and Precision Engineering, 2024, 32(8): 1186

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