| Citation: | ZHANG Yin-hui, ZHUANG Hong, HE Zi-fen, YANG Hong-kuan, HUANG Ying. Lightweight infrared detection of ammonia leakage using shuffle and self-attention[J].Chinese Optics, 2023, 16(3): 607-619.doi:10.37188/CO.2022-0127
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Ammonia gas is an important basic industrial raw material, and realizing its non-contact detection is of great significance for the timely detection of ammonia gas leaks to avoid major safety incidents. Aiming at the shortcoming of conventional ammonia leak detection devices that can only respond when ammonia diffuses to a certain range and makes contact with a sensor, a Shuffling Self-Attention Network (SSANet) model is proposed to realize the infrared non-contact detection of ammonia leaks. Due to the high noise and low contrast of ammonia leakage images obtained by infrared cameras, an infrared detection dataset of ammonia leakage was established through non-local mean denoising and contrast-limited adaptive histogram equalization preprocessing. On the basis of YOLOv5s, the SSANet model uses the K-means algorithm to cluster and analyze the candidate frame suitable for the infrared detection of ammonia gas leakage to preset the model’s parameters. Using the lightweight ShuffleNetv2 network, the depth of 3×3 in the Shuffle Block can be adjusted. The separate convolution kernel is replaced with a 5×5 depth, and the feature extraction network is reconstructed with an SK5 Block containing a new convolution module, which makes the model size, calculation and parameters non-intensive while improving the detection accuracy. The Transformer module is used instead of its original version. The C3 module in the network bottleneck module is replaced by Transformer module to realize the bottom-up fusion of multi-head attention in the leake area, and further improves the detection accuracy. The experimental results show that the size and parameter requirements of the SSANet model are reduced by 76.40% and 78.30%, respectively, to 3.40 M and 1.53 M compared with the basic model of YOLOv5s; the average detection speed of a single image is increased by 1.10% to 3.20 ms; and the average detection accuracy is increased by 3.50% , reaching 96.30%. We provide an effective detection algorithm for the development of a non-contact detection device for ammonia leakage to ensure the safe production and stable operation of ammonia-related enterprises.
| [1] |
杜晓燕, 程五一, 闫瑞青, 等. 我国涉氨制冷企业氨泄漏事故规律性研究[J]. 消防科学与技术,2017,36(6):857-860.
doi:10.3969/j.issn.1009-0029.2017.06.045
DU X Y, CHENG W Y, YAN R Q,
et al. Study on regularity of ammonia leakage accident of ammonia refrigeration enterprises in China[J].
Fire Science and Technology, 2017, 36(6): 857-860. (in Chinese)
doi:10.3969/j.issn.1009-0029.2017.06.045
|
| [2] |
胡继粗, 陈明鹏, 荣茜, 等. 氨气传感材料及器件的研究进展[J]. 功能材料,2019,50(4):4030-4037+4048.
doi:10.3969/j.issn.1001-9731.2019.04.006
HU J C, CHEN M P, RONG Q,
et al. Research progress of ammonia gas sensing materials and devices[J].
Journal of Functional Materials, 2019, 50(4): 4030-4037+4048. (in Chinese)
doi:10.3969/j.issn.1001-9731.2019.04.006
|
| [3] |
克迪里亚·吾麦尔, 姑丽各娜·买买提依明, 买买提艾沙·苏莱曼, 等. 高灵敏复合光波导硫化氢气体传感器的研究[J]. 光学学报,2020,40(24):2428001.
WUMAIER K, MAMTIMIN G, SULAIMAN M,
et al. Highly-sensitive hydrogen-sulfide gas sensor based on composite optical waveguide[J].
Acta Optica Sinica, 2020, 40(24): 2428001. (in Chinese)
|
| [4] |
丁一, 刁泉, 刘东, 等. 石墨烯量子点的合成及其在气体传感中的应用进展[J]. 分析化学,2022,50(4):495-505.
doi:10.19756/j.issn.0253-3820.210843
DING Y, DIAO Q, LIU D,
et al. Synthesis of graphene quantum dots and application in gas sensing[J].
Chinese Journal of Analytical Chemistry, 2022, 50(4): 495-505. (in Chinese)
doi:10.19756/j.issn.0253-3820.210843
|
| [5] |
刘金正, 张立学. 原子层沉积技术在电分析化学中的应用研究进展[J]. 分析化学,2021,49(11):1767-1778.
doi:10.19756/j.issn.0253-3820.210481
LIU J ZH, ZHANG L X. Progress in application of atomic layer deposition technique in electroanalytical chemistry[J].
Chinese Journal of Analytical Chemistry, 2021, 49(11): 1767-1778. (in Chinese)
doi:10.19756/j.issn.0253-3820.210481
|
| [6] |
唐连波, 付大友, 陈琦, 等. 碳量子点增强气液相化学发光检测二氧化碳[J]. 应用化学,2022,39(8):1294-1302.
doi:10.19894/j.issn.1000-0518.210465
TANG L B, FU D Y, CHEN Q,
et al. Enhanced gas-liquid chemiluminescence by carbon dots for determination of carbon dioxide[J].
Chinese Journal of Applied Chemistry, 2022, 39(8): 1294-1302. (in Chinese)
doi:10.19894/j.issn.1000-0518.210465
|
| [7] |
赵鹏鹏, 李庶中, 李迅, 等. 融合视觉显著性和局部熵的红外弱小目标检测[J]. 中国光学,2022,15(2):267-275.
doi:10.37188/CO.2021-0170
ZHAO P P, LI SH ZH, LI X,
et al. Infrared dim small target detection based on visual saliency and local entropy[J].
Chinese Optics, 2022, 15(2): 267-275. (in Chinese)
doi:10.37188/CO.2021-0170
|
| [8] |
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 工程,2019,48(S2):53-65.
ZHANG X, JIN W Q, LI L,
et al. Research progress on passive infrared imaging detection technology and system performance evaluation of natural gas leakage[J].
Infrared and Laser Engineering, 2019, 48(S2): 53-65. (in Chinese)
|
| [9] |
王建平, 李俊山, 杨亚威, 等. 基于红外成像的乙烯气体泄漏检测[J]. 液晶与显示,2014,29(4):623-628.
doi:10.3788/YJYXS20142904.0623
WANG J P, LI J SH, YANG Y W,
et al. Ethylene leaking detection based on infrared imaging[J].
Chinese Journal of Liquid Crystal and Display, 2014, 29(4): 623-628. (in Chinese)
doi:10.3788/YJYXS20142904.0623
|
| [10] |
隋中山, 李俊山, 张姣, 等. 基于张量低秩分解和稀疏表示的红外微小气体释放检测[J]. 光学精密工程,2016,24(11):2855-2862.
SUI ZH SH, LI J SH, ZHANG J,
et al. Micro gas leakage detection based on tensor low rank decomposition and sparse representation from infrared images[J].
Optics and Precision Engineering, 2016, 24(11): 2855-2862. (in Chinese)
|
| [11] |
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 toxic gases based on deep learning algorithm[D]. Hangzhou: Zhejiang Gongshang University, 2018. (in Chinese)
|
| [12] |
翁静, 袁盼, 王铭赫, 等. 基于支持向量机的泄漏气体云团热成像检测方法[J]. 光学学报,2022,42(9):0911002.
doi:10.3788/AOS202242.0911002
WENG J, YUAN P, WANG M H,
et al. Thermal imaging detection method of leak gas clouds based on support vector machine[J].
Acta Optica Sinica, 2022, 42(9): 0911002. (in Chinese)
doi:10.3788/AOS202242.0911002
|
| [13] |
KASTEK M, PIATKOWSKI T, TRZASKAWKA P. Infrared imaging fourier transform spectrometer as the stand-off gas detection system[J].
Metrology&
Measurement Systems, 2011, 18(4): 607-620.
|
| [14] |
BARBER R, RODRIGUEZ-CONEJO M A, MELENDEZ J,
et al. Design of an infrared imaging system for robotic inspection of gas leaks in industrial environments[J].
International Journal of Advanced Robotic Systems, 2015, 12(3): 23.
|
| [15] |
SHI J H, CHANG Y J, XU CH H,
et al. Real-time leak detection using an infrared camera and faster R-CNN technique[J].
Computers&
Chemical Engineering, 2020, 135: 106780.
|
| [16] |
REDMON J, FARHADI A. YOLO9000: Better, Faster, Stronger[C].
IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2017: 7263-7271.
|
| [17] |
REDMON J, FARHADI A. YOLOv3: an incremental improvement[C].
Computer Vision and Pattern Recognition, Springer, 2018: 1804-2767.
|
| [18] |
BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[J/OL]. arXiv: 2004.10934, 2020(2020-04-23).
https://arxiv.org/abs/2004.10934
|
| [19] |
JUBAYER F, SOEB J A, MOJUMDER A N,
et al.. Detection of mold on the food surface using YOLOv5[J].
Current Research in Food Science, 2021, 4: 724-728.
|
| [20] |
LU Y H, ZHANG L W, XIE W. YOLO-compact: an efficient YOLO network for single category real-time object detection[C]//
2020 Chinese Control And Decision Conference (CCDC). IEEE, 2020: 1931-1936.
|
| [21] |
KANUNGO T, MOUNT D M, NETANYAHU N S,
et al. An efficient k-means clustering algorithm: analysis and implementation[J].
IEEE Transactions on Pattern Analysis&
Machine Intelligence, 2002, 24(7): 881-892.
|
| [22] |
DOSOVITSKIY A, BEYER L, KOLESNIKOV A,
et al.. An image is worth 16 x16 words: transformers for image recognition at scale[C].
9th International Conference on Learning Representations, ICLR, 2020.
|
| [23] |
MA N N, ZHANG X Y, ZHENG H T,
et al.. ShuffleNet V2: practical guidelines for efficient CNN architecture design[C].
Proceedings of the 15th European Conference on Computer Vision, Springer, 2018, 122-138.
|
| [24] |
VOITA E, TALBOT D, MOISEEV F,
et al. Analyzing multi-head self-attention: specialized heads do the heavy lifting, the rest can be pruned[J].
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics,
Association for Computational Linguistics, 2019: 5797-5808.
|
| [25] |
XIONG R B, YANG Y C, HE D,
et al.. On layer normalization in the transformer architecture[C]//
Proceedings of the 37th International Conference on Machine Learning, PMLR, 2020: 10524-10533.
|
| [26] |
BUADES A, COLL B, MOREL J M. A non-local algorithm for image denoising[C]//
2005 IEEE Computer Society Conference On Computer Vision and Pattern Recognition, IEEE, 2005, 2: 60-65.
|
| [27] |
REZA A M. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement[J].
Journal of VLSI Signal Processing Systems for Signal,
Image and Video Technology, 2004, 38(1): 35-44.
|
| [28] |
江泽涛, 钱艺, 伍旭, 等. 一种基于ARD-GAN的低照度图像增强方法[J]. 电子学报,2021,49(11):2160-2165.
JIANG Z T, QIAN Y, WU X,
et al. Low-light image enhancement method based on ARD-GAN[J].
Acta Electronica Sinica, 2021, 49(11): 2160-2165. (in Chinese)
|
| [29] |
刘柯, 李旭健. 水下和微光图像的去雾及增强方法[J]. 光学学报,2020,40(19):1910003.
doi:10.3788/AOS202040.1910003
LIU K, LI X J. De-hazing and enhancement methods for underwater and low-light images[J].
Acta Optica Sinica, 2020, 40(19): 1910003. (in Chinese)
doi:10.3788/AOS202040.1910003
|
| [30] |
江巨浪, 刘国明, 朱柱, 等. 基于快速模糊聚类的动态多直方图均衡化算法[J]. 电子学报,2022,50(1):167-176.
doi:10.12263/DZXB.20201040
JIANG J L, LIU G M, ZHU ZH,
et al. Dynamic multi-histogram equalization Based on fast fuzzy clustering[J].
Acta Electronica Sinica, 2022, 50(1): 167-176. (in Chinese)
doi:10.12263/DZXB.20201040
|
| [31] |
HAN K, WANG Y H, TIAN Q,
et al.. GhostNet: more features from cheap operations[C].
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020: 1577-1586.
|
| [32] |
HOWARD A, SANDLER M, CHEN B,
et al.. Searching for MobileNetV3[C].
2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2020: 1314-1324
|
| [33] |
GE ZH, LIU S T, WANG F, et al.. Yolox: exceeding yolo series in 2021[J/OL]. arXiv: 2107.08430, 2021(2021-08-06).
https://arxiv.org/abs/2107.08430
|
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