Advanced Search
中文
Volume 45 Issue 11
Nov.  2023
Turn off MathJax
Article Contents
Abstract
References
Related Articles
WANG Hongjun, YANG Yiming, ZHAO Hui, YUE Youjun. Infrared and Visible Image Fusion of Unmanned Agricultural Machinery Based on PIE and CGAN[J]. Infrared Technology , 2023, 45(11): 1223-1229.
Citation: WANG Hongjun, YANG Yiming, ZHAO Hui, YUE Youjun. Infrared and Visible Image Fusion of Unmanned Agricultural Machinery Based on PIE and CGAN[J]. Infrared Technology , 2023, 45(11): 1223-1229.
shu

Infrared and Visible Image Fusion of Unmanned Agricultural Machinery Based on PIE and CGAN

  • School of Electrical Engineering and Automation, Tianjin University of Technology/Tianjin Key Laboratory of Complex System Control Theory and Application, Tianjin 300384, China

More Information
  • Received Date: June 13, 2022
  • Revised Date: August 09, 2022
    Graphical Abstract
    • Abstract
  • In this study, we proposed an infrared and visible image fusion algorithm that combines PIE and CGAN to make unmanned agricultural machinery perceive environmental information promptly and avoid accidents during production in complex environments. First, we trained the CGAN using an infrared image and corresponding saliency regions. The infrared image is input into the trained network to obtain the saliency region mask. After morphological optimization, we performed image fusion based on the PIE. Finally, we enhanced the fusion results by contrast processing. This algorithm can realize fast image fusion and satisfy the requirements for real-time environmental perception of unmanned agricultural machines. In addition, the algorithm retains the details of visible images and highlights important information concerning humans and animals in infrared images. It performs well in standard deviation and information entropy.
    • FullText(HTML)
    • References (20)
  • [1]
    郑国伟. 《中国制造2025》简介与相关情况[J]. 中国仪器仪表, 2018(10): 25-28. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYQB201810004.htm

    ZHENG Guowei. Introduction and related situation of "Made in China 2025" [J]. Chinese Instrumentation, 2018(10): 25-28. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYQB201810004.htm
    [2]
    安影. 基于多尺度分解的红外与可见光图像融合算法研究[D]. 西安: 西北大学, 2020. Doi: 10.27405/d.cnki.gxbdu.2020.000953.

    Anying. Study on infrared and visible light image fusion algorithms based on multi -scale decomposition[D]. Xi'an: Northwest University, 2020. Doi: 10.27405/d.cnki.gxbdu.2020.000953.
    [3]
    CHEN Jun, LI Xuejiao, LUO Linbo, et al. Infrared and visible image fusion based on target-enhanced multiscale transform decomposition[J]. Information Sciences, 2020, 508: 64-78. DOI: 10.1016/j.ins.2019.08.066
    [4]
    LI G, LIN Y, QU X. An infrared and visible image fusion method based on multi-scale transformation and norm optimization[J]. Information Fusion, 2021, 71(2): 109-129.
    [5]
    ZHANG S, LI X, ZHANG X, et al. Infrared and visible image fusion based on saliency detection and two-scale transform decomposition[J]. Infrared Physics & Technology, 2021, 114(3): 103626.
    [6]
    王海宁, 廖育荣, 林存宝, 等. 基于改进生成对抗网络模型的红外与可见光图像融合[J/OL]. 电讯技术, [2022-06-08]. http://kns.cnki.net/kcms/detail/51.1267.tn.20220509.1228.004.html.

    WANG Haining, LIAO Yurong, LIN Cunbao, et al. Based on the integration of infrared and visible light images that are improved to generate network models [J/OL]. Telecommunications Technology, [2022-06-08]. http://kns.cnki.net/kcms/detail/51.1267.tn.20220509.1228.004.html.
    [7]
    孙佳敏, 宋慧慧. 基于DWT和生成对抗网络的高光谱多光谱图像融合[J]. 无线电工程, 2021, 51(12): 1434-1441. https://www.cnki.com.cn/Article/CJFDTOTAL-WXDG202112008.htm

    SUN Jiamin, SONG Huihui. Hyperspectral multispectral image fusion based on DWT and generative adversarial network[J]. Radio Engineering, 2021, 51(12): 1434-1441. https://www.cnki.com.cn/Article/CJFDTOTAL-WXDG202112008.htm
    [8]
    武圆圆, 王志社, 王君尧, 等. 红外与可见光图像注意力生成对抗融合方法研究[J]. 红外技术, 2022, 44(2): 170-178. http://hwjs.nvir.cn/article/id/7f2ae6e4-af9c-4929-a689-cb053b4dda85

    WU Yuanyuan, WANG Zhishe, WANG Junyao, et al. Infrared and visible light image attention generating confrontation fusion methods [J]. Infrared Technology, 2022, 44(2): 170-178. http://hwjs.nvir.cn/article/id/7f2ae6e4-af9c-4929-a689-cb053b4dda85
    [9]
    Hussain K F, Mahmoud R. Efficient poisson image editing[J]. Electronic Letters on Computer Vision and Image Analysis, 2015, 14(2): 45-57.
    [10]
    Chandani P, Nayak S. Generative adversarial networks: an overview[J]. Journal of Critical Reviews, 2020, 7(3): 753-758.
    [11]
    MOON S. ReLU network with bounded width is a universal approximator in view of an approximate identity[J]. Applied Sciences, 2021, 11(1): 427-427. DOI: 10.3390/app11010427
    [12]
    WU S, LI G, DENG L, et al. L1-norm batch normalization for efficient training of deep neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(7): 2043-2051. DOI: 10.1109/TNNLS.2018.2876179
    [13]
    Abdeimotaal H, Abdou A, Omar A, et al. Pix2pix conditional generative adversarial networks for scheimpflug camera color-coded corneal tomography image generation[J]. Translational Vision Science and Technology, 2021, 10(7): 21-21.
    [14]
    甄媚, 王书朋. 可见光与红外图像自适应加权平均融合方法[J]. 红外技术, 2019, 41(4): 341-346. http://hwjs.nvir.cn/article/id/hwjs201904008

    ZHEN Mei, WANG Shupeng. Visible light and infrared images adaptive weighted average fusion method[J]. Infrared Technology, 2019, 41(4): 341-346. http://hwjs.nvir.cn/article/id/hwjs201904008
    [15]
    张影. 卫星高光谱遥感农作物精细分类研究[D]. 北京: 中国农业科学院, 2021. DOI: 10.27630/d.cnki.gznky.2021.000383.

    ZHANG Ying. Satellite High Spectrum Remote Sensing Crop Fine Classification Study[D]. Beijing: Chinese Academy of Agricultural Sciences, 2021. Doi: 10.27630/d.cnki.gznky.2021.000383.
    [16]
    倪钏. 红外与可见光图像融合方法研究[D]. 温州: 温州大学, 2020. Doi: 10.27781/d.cnki.gwzdx.2020.000124.

    NI Yan. Research on the Fusion Method of Infrared and Visible Light Image[D]. Wenzhou: Wenzhou University, 2020. Doi: 10.27781/d.cnki.gwzdx.2020.000124.
    [17]
    CHEN J, LI X, LUO L, et al. Infrared and visible image fusion based on target-enhanced multiscale transform decomposition[J]. Information Sciences, 2020, 508: 64-78.
    [18]
    刘娜, 曾小晖. 基于信息熵引导耦合复杂度调节模型的红外图像增强算法[J]. 国外电子测量技术, 2021, 40(12): 37-43. Doi: 10.19652/j.cnki. femt.2102956.

    LIU Na, ZENG Xiaohui. Based on information entropy guidance coupling complexity adjustment model of infrared image enhancement algorithm [J]. Foreign Electronic Measurement Technology, 2021, 40(12): 37-43. Doi: 10.19652/J.CNKI.FEMT.2102956.
    [19]
    KONG X, LIU L, QIAN Y, et al. Infrared and visible image fusion using structure-transferring fusion method[J]. Infrared Physics & Technology, 2019, 98: 161-173.
    [20]
    王瑜婧. 显著性检测的红外与可见光图像融合算法研究[D]. 西安: 西安科技大学, 2021. Doi: 10.27397/d.cnki.gxaku.2021.000608.

    WANG Yujing. Research on Infrared and Visible Light Image Fusion Algorithms of Significant Detection[D]. Xi'an: Xi'an University of Science and Technology, 2021. Doi: 10.27397/d.cnki.gxaku.2021.000608.
    • Related Articles

  • Related Articles

    [1]LI Jianghui. A Method and System for Infrared Image Simulation Based on ModelSim[J]. Infrared Technology , 2024, 46(7): 802-806.
    [2]WANG Xia, ZHAO Jiabi, SUN Qiyang, JIN Weiqi. Performance Evaluation Model for Infrared Polarization Imaging System[J]. Infrared Technology , 2023, 45(5): 437-445.
    [3]KONG Derui, XIA Ming, LI Haiying, CHEN Jun, ZHAO Peng. Theoretical Analysis and Matlab Simulation of Dynamic Vibration Absorber for Single-Piston Linear Compressor[J]. Infrared Technology , 2021, 43(10): 1014-1021.
    [4]ZHANG Jingyang, YAN Limin, CHEN Zhiheng. Nighttime Fog Removal Using the Dark Point Light Source Model[J]. Infrared Technology , 2021, 43(8): 798-803.
    [5]HU Yang, CHEN Cheng, HUA Sangtun, QIU Yafeng. Thermal Calculation of Countercurrent Cooling Tower and Design of Infrared Thermal Image Temperature Control System[J]. Infrared Technology , 2021, 43(3): 225-229.
    [6]PAN Hao, MA Yi, ZHOU Fangrong, MA Yutang, QIAN Guochao, WEN Gang. Research on the Theoretical Model Between Solar-blind UV and Atmospheric Temperature during Atmospheric Transmission[J]. Infrared Technology , 2020, 42(10): 1007-1012.
    [7]HAN Kun, YAO Ze, QIAO Kai, YANG Shuning, HE Yingping. Theoretical Model of Dynamic MTF of Low-Light-Level ICCD[J]. Infrared Technology , 2020, 42(3): 294-299.
    [8]SUN Jianning, SI Shuguang, WANG Xingchao, JIN Muchun, LI Dong, REN Ling, HOU Wei, ZHAO Min, GU Ying, QIAO Fangjian, ZHANG Haoda, CAO Yiqi. Preparation Method of K2CsSb Photocathode Using the Reflectance Theory Model[J]. Infrared Technology , 2017, 39(12): 1087-1091.
    [9]ZHANG Yao-jun, WU Gui-ling, LI Lei. Fusion for Infrared and Visible Light Images Based on Shearlet Transform and Quantum Theory Model[J]. Infrared Technology , 2015, (5): 418-423.
    [10]Theoretic Module of Uncooled IR Detector Performance Improvement[J]. Infrared Technology , 2002, 24(4): 31-34. DOI: 10.3969/j.issn.1001-8891.2002.04.009

  • Cited by

    Periodical cited type(1)

    1. 邱祥彪，杨晓明，孙建宁，王健，丛晓庆，金戈，曾进能，张正君，潘凯，陈晓倩. 高空间分辨微通道板现状及发展. 红外技术. 2024(04): 460-466 . 本站查看

    Other cited types(0)

Catalog

    Get Citation
    PDF
    XML
    Article views (123) PDF downloads (28) Cited by(1)
    Related

    Copyright © 《Infrared Technology》Editorial Office京ICP备000000000号-1

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return