Infrared Image Segmentation of Methane Leaks Incorporating Attentional Branching Features

HE Zifen; CAO Huizhu; ZHANG Yinhui; HUANG Junxuan; SHI Benjie; ZHU Shouye

Volume 45 Issue 4

Apr. 2023

Turn off MathJax

Article Contents

Abstract

References

Infrared Technology > 2023 > 45(4): 417-426.

HE Zifen, CAO Huizhu, ZHANG Yinhui, HUANG Junxuan, SHI Benjie, ZHU Shouye. Infrared Image Segmentation of Methane Leaks Incorporating Attentional Branching Features[J]. Infrared Technology , 2023, 45(4): 417-426.

Citation:

PDF (1247 KB)

Infrared Image Segmentation of Methane Leaks Incorporating Attentional Branching Features

Faculty of Mechatronics and Electrical Engineering, Kunming University of Science and Technology, Kunming 650000, China

More Information

Received Date: April 03, 2022
Revised Date: May 10, 2022

Graphical Abstract

Abstract

Abstract

Methane is an important energy source for modern industrial production and social life, and its effective detection and segmentation are important for the timely detection of methane leaks and identification of its diffusion range. To address image problems such as blurred contours of methane gas, low contrast between leaking methane gas and the background, and susceptibility of the shape to atmospheric flow factors under infrared imaging conditions, this study proposes an infrared image segmentation network (attention branch feature network (ABFNet)) incorporating attention branch features to achieve methane gas leak detection. First, to enhance the feature extraction capability of the model for IR methane gas images, a branch feature fusion module was designed to fuse the output features of residual modules 1 and 2 with residual module 3 in a pixel-by-pixel summing method to obtain rich and detailed feature expressions of IR methane gas images to improve the model's recognition accuracy. Second, to further accelerate the inference speed of the model, the 3×3 convolution in the standard bottleneck unit was replaced with a depth-separable convolution to significantly reduce the number of parameters required for real-time methane gas leak detection. Finally, scSE attention mechanisms were embedded in the branching feature fusion module to focus more on the edge and center semantic information of the diffusion region to overcome the problem of low contrast of the blurred IR methane gas contours and improve the generalization ability of the model. The experimental results showed that the quantitative segmentation accuracy of the proposed ABFNet models AP50@95, AP50, and AP60 reached 38.23%, 89.63%, and 75.33%, respectively, with improvements of 4.66%, 3.76%, and 7.04%, respectively, compared with the segmentation accuracy of the original YOLACT model. The inference speed reached 34.99 frames/s and met the demand of real-time detection. The experimental results verified the effectiveness and engineering practicality of the proposed algorithm for infrared methane leak detection.
- infrared image segmentation,
- methane leaks,
- attentional branching features,
- real-time detection

FullText(HTML)

References (24)

References

[1]	朱亮, 高少华, 丁德武, 等. 光学气体成像技术在泄漏检测与维修中的应用研究[J]. 安全、健康和环境, 2014, 14(4): 14-16. https://www.cnki.com.cn/Article/CJFDTOTAL-SAFE201404004.htm ZHU L, GAO S H, DING D W, et al. Study on the application of optical gas imaging technology in leak detection and repair[J]. Safety, Health and Environment, 2014, 14(4): 14-16. https://www.cnki.com.cn/Article/CJFDTOTAL-SAFE201404004.htm
[2]	屈姣. 甲烷和煤尘爆炸特性实验研究[D]. 西安: 西安科技大学, 2015. QU J. Experimental study on the explosion characteristics of methane and coal dust[D]. Xi'an: Xian University of Science and Technology, 2015.
[3]	张旭, 金伟其, 李力, 等. 天然气泄漏被动式红外成像检测技术及系统性能评价研究进展[J]. 红外与激光工程, 2019, 48(S2): 53-65. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ2019S2008.htm ZHANG X, JIN W Q, LI L, et al. Research progress on passive infrared imaging detection technology and system performance evaluation for natural gas leakage[J]. Infrared and Laser Engineering, 2019, 48(S2): 53-65. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ2019S2008.htm
[4]	孟宗. 透射式光纤甲烷气体监测系统的研究[J]. 仪器仪表学报, 2005(S1): 49-50. https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB2005S1019.htm MENG Z. Research on transmission type fiber optic methane gas monitoring system[J]. Journal of Instrumentation, 2005(S1): 49-50. https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB2005S1019.htm
[5]	HU L, ZHENG C, ZHANG M, et al. Long-distance in-situ methane detection using near-infrared light-induced thermo-elastic spectroscopy[J]. Photoacoustics, 2020, 21: 100230.
[6]	苑彬, 陈书立. 红外吸收光谱型城市路网尾气浓度检测传感器[J]. 电子器件, 2020, 43(2): 471-476. DOI: 10.3969/j.issn.1005-9490.2020.02.042 YUAN B, CHENG S L. Infrared absorption spectroscopy-based urban road network exhaust concentration detection sensor[J]. Electronic Devices, 2020, 43(2): 471-476. DOI: 10.3969/j.issn.1005-9490.2020.02.042
[7]	迟晓铭, 肖安山, 朱亮, 等. 石化企业气体泄漏红外成像检测技术研究进展[J]. 安全、健康和环境, 2021, 21(2): 1-5. https://www.cnki.com.cn/Article/CJFDTOTAL-SAFE202102001.htm CHI X Q, XIAO AN S, ZHU L, et al. Research progress of infrared imaging detection technology for gas leakage in petrochemical enterprises[J]. Safety, Health and Environment, 2021, 21(2): 1-5. https://www.cnki.com.cn/Article/CJFDTOTAL-SAFE202102001.htm
[8]	洪少壮. 基于视觉的红外气体泄漏检测算法研究[D]. 大连: 大连海事大学, 2020. HONG S Z. Research on Infrared Gas Leak Detection Algorithm Based on Vision[D]. Dalian: Dalian Maritime University, 2020.
[9]	熊仕富. 红外热成像甲烷气体探测与识别系统关键技术研究[D]. 长春: 长春理工大学, 2018. XIONG S F. Research on Key Technologies of Infrared Thermal Imaging Methane Gas Detection and Identification System[D]. Changchun: Changchun University of Science and Technology, 2018.
[10]	王云云. 基于红外多光谱的有毒有害气体被动遥测方法研究[D]. 合肥: 中国科学技术大学, 2021. WANG Y Y. Research on Passive Telemetry of Toxic and Hazardous Gases Based on Infrared Multispectrum[D]. Hefei: University of Science and Technology of China, 2021.
[11]	方鹏, 郝宏运, 李腾飞, 等. 基于注意力机制和可变形卷积的鸡只图像实例分割提取[J]. 农业机械学报, 2021, 52(4): 257-265. https://www.cnki.com.cn/Article/CJFDTOTAL-NYJX202104027.htm FANG P, HAO H Y, LI T F, et al. Extraction of chicken image instance segmentation based on attention mechanism and deformable convolution[J]. Journal of Agricultural Machinery, 2021, 52(4): 257-265. https://www.cnki.com.cn/Article/CJFDTOTAL-NYJX202104027.htm
[12]	林云. 基于深度学习的有害气体红外图像处理研究[D]. 杭州: 浙江工商大学, 2018. LIN Y. Research on infrared image processing of harmful gases based on deep learning[D]. Hangzhou: Zhejiang University of Technology and Industry, 2018.
[13]	ZENG J, LIN Z, QI C, et al. An improved object detection method based on deep convolution neural network for smoke detection[C]// International Conference on Machine Learning and Cybernetics (ICMLC), 2018: 184-189.
[14]	Bolya D, Zhou C, Xiao F, et al. Yolact: Real-time instance segmentation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 9157-9166.
[15]	XIE S, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1492-1500.
[16]	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[J]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 770-778.
[17]	韩明, 王景芹, 王敬涛, 等. 级联特征融合孪生网络目标跟踪算法研究[J]. 计算机工程与应用, 2022, 58(6): 11. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202206021.htm HAN M, WANG J J, WANG J T, et al. Research on cascaded feature fusion twin network target tracking algorithm[J]. Computer Engineering and Applications, 2022, 58(6): 11. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202206021.htm
[18]	何自芬, 黄俊璇, 刘强, 等. 基于非对称混洗卷积神经网络的苹果叶部病害分割[J]. 农业机械学报, 2021, 52(8): 221-230. https://www.cnki.com.cn/Article/CJFDTOTAL-NYJX202108022.htm HE Z F, HUANG J X, LIU Q, et al. Apple leaf disease segmentation based on asymmetric mixed wash convolutional neural network[J]. Journal of Agricultural Machinery, 2021, 52(8): 221-230. https://www.cnki.com.cn/Article/CJFDTOTAL-NYJX202108022.htm
[19]	Chollet F. Xception: Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1251-1258.
[20]	Roy A G, Navab N, Wachinger C. Concurrent spatial and channel 'squeeze & excitation' in fully convolutional networks[C]//Medical Image Computing and Computer Assisted Intervention-MICCAI, 2018: 421-429.
[21]	HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.
[22]	WANG J, Tchapmi L P, Ravikumar A P, et al. Machine vision for natural gas methane emissions detection using an infrared camera[J]. Applied Energy, 2020, 257: 113998.
[23]	LIU H, Soto R A R, Xiao F, et al. YOLACT edge: Real-time instance segmentation on the edge[C]//IEEE International Conference on Robotics and Automation (ICRA), 2021: 9579-9585.
[24]	HE K, Gkioxari G, Dollár P, et al. Mask R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2961-2969.

[1]	ZHANG Kunjie. Research Progress and Trends of High Operating Temperature Infrared Detectors[J]. Infrared Technology , 2021, 43(8): 766-772.
[2]	Military Status and Trends of Infrared Common Modules in Europe and America[J]. Infrared Technology , 2020, 42(7): 697-701.
[3]	Current Status and Trend of Vehicle Photoelectric Mast Technology[J]. Infrared Technology , 2020, 42(6): 519-527.
[4]	LI Yu, LU Qiang, Bai Piji. Situation and Trend of Uncooled Infrared Imaging Systems for Army Equipment Worldwide[J]. Infrared Technology , 2017, 39(7): 581-593.
[5]	Development Situation and Trend of InSb Infrared Focal Plane Array[J]. Infrared Technology , 2016, 38(11): 905-913.
[6]	GUO Jiaqi, ZHANG Zhigang, HU Zebin, YAN Huanmin. Advance and Trend of Near Infrared Camouflage Technology[J]. Infrared Technology , 2016, 38(2): 96-101.
[7]	The Status and Development Trend of Infrared Image Processing Technology[J]. Infrared Technology , 2013, (6): 311-318.
[8]	LU Jian-ming, CAI Yi, HUANG Hui. Status、Features and Trends of Infrared Imaging Technology in Russia[J]. Infrared Technology , 2006, 28(2): 68-73. DOI: 10.3969/j.issn.1001-8891.2006.02.002
[9]	SUN Shi-an, FEI Yi-wei, WANG Xie-qi, SUN Yuan-bao, PENG Xiu-hua. IR Smoke Screen Material and Its Developing Trends[J]. Infrared Technology , 2005, 27(2): 164-166. DOI: 10.3969/j.issn.1001-8891.2005.02.016
[10]	Layering Voting Target Detection Method and Performance Analyzing[J]. Infrared Technology , 2003, 25(6): 10-13,17. DOI: 10.3969/j.issn.1001-8891.2003.06.003

Cited By

Cited by

Periodical cited type(2)

1.	黄坤琳，吴国周，徐维新，李利东，王海梅，李航，李自翔，司荆柯，刘洪宾，吴成娜. 呼伦贝尔东部农田区动态融雪过程及其影响因子. 干旱区研究. 2024(09): 1514-1526 .
2.	黄林，李晖，康璇. 基于Freeman全极化分解的干雪识别指数模型构建. 厦门理工学院学报. 2023(05): 40-48 .

Other cited types(2)

Get Citation

PDF

XML

Article views PDF downloads Cited by(4)

Infrared Image Segmentation of Methane Leaks Incorporating Attentional Branching Features

Abstract

References

Related Articles

Cited by

Periodical cited type(2)

Other cited types(2)

Catalog

Related

Infrared Image Segmentation of Methane Leaks Incorporating Attentional Branching Features

Abstract

References

Related Articles

Cited by

Periodical cited type(2)

Other cited types(2)

Catalog

Related

Export File

Citation

Format

Content