Advanced Search
中文
Volume 45 Issue 6
Jun.  2023
Turn off MathJax
Article Contents
Abstract
References
Related Articles
YAN Yunbin, CUI Bolun, YANG Tingting, LI Xin, SHI Zhicheng, DUAN Pengfei, SONG Meiping, LIAN Minlong. Multi-modal High-Resolution Hyperspectral Object Detection System Based on Lightweight Platform[J]. Infrared Technology , 2023, 45(6): 582-591.
Citation: YAN Yunbin, CUI Bolun, YANG Tingting, LI Xin, SHI Zhicheng, DUAN Pengfei, SONG Meiping, LIAN Minlong. Multi-modal High-Resolution Hyperspectral Object Detection System Based on Lightweight Platform[J]. Infrared Technology , 2023, 45(6): 582-591.
shu

Multi-modal High-Resolution Hyperspectral Object Detection System Based on Lightweight Platform

  • 1.

    Beijing Institute of Space Mechanics & Electricity, Beijing 100094, China

  • 2.

    College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China

More Information
  • Received Date: February 21, 2023
  • Revised Date: April 27, 2023
    Graphical Abstract
    • Abstract
  • To solve the problems of large volume and low detection efficiency of UAV hyperspectral imagers, a light and small multi-modal high-resolution hyperspectral imager is proposed. This study primarily introduces the optical system design, data acquisition, and real-time processing modules of the hyperspectral imager. The proposed imager satisfies the detection mode requirements in different fields, such as spectral characteristic analysis and target detection, by switching the scanning mode. A low distortion, high throughput, and compact spectroscopic optical system design is adopted to meet the weight and detection accuracy requirements of the UAV platform for the spectral imager. The product was processed according to the design requirements and the performance was tested simultaneously. Among these, the MTF reached 0.19 and spectral resolution was 3.5–5.4 nm. The ability of the system to detect real-time targets was verified through detection of impurities in various pipelines. The implementation results showed that the system achieved high-precision spectral anomaly target detection in 2048 pixel×2048 pixel scenes per second with a detection accuracy greater than 87%.
    • FullText(HTML)
    • References (30)
  • [1]
    Cocks T, Jenssen R, Stewart A, et al. The HyMap airborne hyperspectral sensor: the system, calibration and performance[C]//Proc. 1st EARSeL Workshop on Imaging Spectroscopy, 1998: 37-42.
    [2]
    Babey S K, Anger C D. Compact airborne spectrographic imager (CASI)[C]//Imaging Spectrometry of the Terrestrial Environment, 1930, DOI: 10.1117/12.157052.
    [3]
    Ulbrich G J, Meynart R, Nieke J. APEX-airborne prism experiment: the realization phase of an airborne hyperspectral imager[C]//Proceedings of SPIE-The International Society for Optical Engineering, 2004, 5570: 453-459.
    [4]
    Hamlin L, Green R O, Mouroulis P, et al. Imaging spectrometer science measurements for Terrestrial Ecology: AVIRIS and new developments[C]//Aerospace Conference. IEEE, 2011: 1-7.
    [5]
    Pullanagari R R, Kereszturi G, Yule I J. Quantification of dead vegetation fraction in mixed pastures using AisaFENIX imaging spectroscopy data[J]. International Journal of Applied Earth Observation & Geoinformation, 2017, 58: 26-35.
    [6]
    WANG Y. Wide-field-of-view visible and near infrared pushbroom airborne hyperspectral imager (Conference Presentation)[C]// Infrared Technology and Applications XLIV, 2018, 10624: 15-19.
    [7]
    Horstrand P, Guerra R, Rodriguez A, et al. A UAV platform based on a hyperspectral sensor for image capturing and on-board processing[J]. IEEE Access, 2019, 7: 66919-66938. DOI: 10.1109/ACCESS.2019.2913957.
    [8]
    QIN Jianwei, CHAO Kuanglin, Moon S Kim, et al. Hyperspectral and multispectral imaging for evaluating food safety and quality[J]. Journal of Food Engineering, 2013, 118(2): 157-171. DOI: 10.1016/j.jfoodeng.2013.04.001
    [9]
    Barreto M, Johansen K, Angel Y, et al. Radiometric assessment of a UAV-Based push-broom hyperspectral camera[J]. Sensors, 2019, 19(21): 4699. DOI: 10.3390/s19214699
    [10]
    Malenovsky Z, Lucieer A, Robinson S A, et al. Ground-based imaging spectroscopy data for estimation of Antarctic moss relative vigour from remotely sensed chlorophyll content and leaf density at ASPA[J]. Environmental Science, 2015, DOI: 10.4225/15/555C1DB80CB70.
    [11]
    Kanning M, I Kühling, Trautz D, et al. High-resolution UAV-based hyperspectral imagery for LAI and chlorophyll estimations from wheat for yield prediction[J]. Remote Sensing, 2018, 10(12): 2000. DOI: 10.3390/rs10122000
    [12]
    ZHU C, Kanaya Y, Tsuchiya M, et al. Optimization of a hyperspectral imaging system for rapid detection of microplastics down to 100 m[J]. Methods X, 2021, 8: 101175.
    [13]
    Lenhard, Karim, Schwarzmaier, et al. Independent laboratory character-rization of NEO HySpex imaging spectrometers VNIR-1600 and SWIR-320m-e[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4): 1828-1841. DOI: 10.1109/TGRS.2014.2349737
    [14]
    Blaaberg S, T Løke, Baarstad I, et al. A next generation VNIR-SWIR hyperspectral camera system: HySpex ODIN-1024[C]//Electro-optical & Infrared Systems: Technology & Applications XI. International Society for Optics and Photonics, 2014, DOI: 10.1117/12.2067497.
    [15]
    Telmo A, Hruka Joná, Pádua Luís, et al. Hyperspectral imaging: a review on UAV-based sensors, data processing and applications for agriculture and forestry[J]. Remote Sensing, 2017, 9(11): 1110. DOI: 10.3390/rs9111110
    [16]
    Nex F, Armenakis C, Cramer M, et al. UAV in the advent of the twenties: Where we stand and what is next[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 184: 215-242. DOI: 10.1016/j.isprsjprs.2021.12.006
    [17]
    刘银年. "高分五号"卫星可见短波红外高光谱相机的研制[J]. 航天返回与遥感, 2018, 39(3): 25-28. https://www.cnki.com.cn/Article/CJFDTOTAL-HFYG201803004.htm

    LIU Y N. Visible-shortwave infrared hyperspectral imager of GF-5 satellite[J]. Spacecraft Recovery & Remote Sensing, 2018, 39(3): 25-28. https://www.cnki.com.cn/Article/CJFDTOTAL-HFYG201803004.htm
    [18]
    Stefano P, Angelo P, Simone P, et al. The PRISMA hyperspectral mission: Science activities and opportunities for agriculture and land monitoring[C]//Geoscience & Remote Sensing Symposium. IEEE, 2014: 4558-4561.
    [19]
    Iwasaki A, Tanii J, Kashimura O, et al. Prelaunch status of hyperspectral imager suite (Hisui)[C]//IEEE International Geoscience and Remote Sensing Symposium, 2019: 5887-5890.
    [20]
    郭俊先. 基于高光谱成像技术的棉花杂质检测方法的研究[D]. 杭州: 浙江大学, 2011.

    GUO J X. Study on Detection of Cotton Trashes by Hyperspectral Imaging[D]. Hangzhou: Zhejiang University, 2011.
    [21]
    汪瑶. 用于工业分拣的高光谱智能相机研究[D]. 合肥: 中国科学技术大学, 2012.

    WANG Y. Research on Hyperspectral Smart Camera for Industrial Sorting[D]. Heifei: University of Science and Technology of China, 2012.
    [22]
    Faqeerzada M A, Lohumi S, Kim G, et al. Hyperspectral shortwave infrared image analysis for detection of adulterants in almond powder with one-class classification method[J]. Sensors, 2020, 20(20): 5855. DOI: 10.3390/s20205855
    [23]
    YU F H, BAI J C, JIN Z Y, et al. Research on precise fertilization method of rice tillering stage based on UAV hyperspectral remote sensing prescription map[J]. Agronomy, 2022, 12(11): 2893. DOI: 10.3390/agronomy12112893
    [24]
    LIU X M, WANG H C, CAO Y W, et al. Comprehensive growth index monitoring of desert steppe grassland vegetation based on UAV hyperspectral[J]. Front Plant Sci. , 2023, 13: 1050999. DOI: 10.3389/fpls.2022.1050999.
    [25]
    Resonon Inc. Hyperspectral Imaging Solutions[EB/OL]. [2023-06-12]. https://resonon.com.
    [26]
    李岩, 马越. 扫描线校正器校正量的实验室测试方法[J]. 航天返回与遥感, 2014, 35(2): 62-68. https://www.cnki.com.cn/Article/CJFDTOTAL-HFYG201402009.htm

    LI Y, MA Y. The laboratory scan line corrector test method[J]. Spacecraft Recovery & Remote Sensing, 2014, 35(2): 62-68. https://www.cnki.com.cn/Article/CJFDTOTAL-HFYG201402009.htm
    [27]
    ZENG Y, HAO D, Huete A, et al. Optical vegetation indices for monitoring terrestrial ecosystems globally[J]. Nature Reviews Earth & Environment, 2022(3): 477-493.
    [28]
    WANG Y, WANG L, YU C, et al. Constrained-target band selection for multiple-target detection[J]. IEEE transactions on geoscience and remote sensing: a publication of the IEEE Geoscience and Remote Sensing Society, 2019: 6079-6103, DOI: 10.1109/TGRS.2019.2904264.
    [29]
    Zabalza J, QING C, YUEN P, et al. Fast implementation of two-dimensional singular spectrum analysis for effective data classification in hyperspectral imaging[J]. Journal of the Franklin Institute, 2018, 355(4): 1733-1751. DOI: 10.1016/j.jfranklin.2017.05.020
    [30]
    Berk A, Anderson G P, Acharya P K, et al. MODTRAN (TM) 5, a reformulated atmospheric band model with auxiliary species and practical multiple scattering options: Update[C]//Proceedings of SPIE-The International Society for Optical Engineering, 2005, 5806: 662-667.
    • Related Articles

  • Related Articles

    [1]CHEN Xiaohan, XU Yuanyuan. Infrared Multi-Scale Target Detection Algorithm Based on RCR-YOLO[J]. Infrared Technology , 2025, 47(4): 459-467.
    [2]LIU Xin, ZHANG Bin. Electronic Zooming of Infrared Image Based on Lightweight Multi-scale Aggregation Network[J]. Infrared Technology , 2025, 47(4): 445-452.
    [3]YE Baicheng, ZHU Youpan, ZHOU Yongkang, DUAN Chenhao, ZHANG Yudong, TAO Zhigang, FU Zhiyu. Review of Lightweight Target Detection Algorithms[J]. Infrared Technology , 2025, 47(3): 289-298.
    [4]CHEN Yonglin, WANG Hengtao, ZHANG Shang. Lightweight Infrared Target Detection Algorithm Based on YOLO v7[J]. Infrared Technology , 2024, 46(12): 1380-1389.
    [5]SHAO Yanhua, HUANG Qimeng, MEI Yanying, ZHANG Xiaoqiang, CHU Hongyu, WU Yadong. Multi-scale Anchor Construction Method for Object Detection[J]. Infrared Technology , 2024, 46(2): 162-167.
    [6]ZHOU Jinjie, JI Li, ZHANG Qian, ZHANG Baohui, YUAN Xilin, LIU Yanqing, YUE Jiang. Multiscale Infrared Object Detection Network Based on YOLO-MIR Algorithm[J]. Infrared Technology , 2023, 45(5): 506-512.
    [7]CHEN Yanlin, WANG Zhishe, SHAO Wenyu, YANG Fan, SUN Jing. Multi-scale Transformer Fusion Method for Infrared and Visible Images[J]. Infrared Technology , 2023, 45(3): 266-275.
    [8]SUN Shixin, ZHENG Zhiyun. Genetic Algorithm for Infrared Multi-target Detection Based on Multi-scale NNLoG Feature[J]. Infrared Technology , 2019, 41(9): 837-842.
    [9]SHEN Xu, CHENG Xiaohui, WANG Xinzheng. Infrared Dim-small Object Detection Algorithm Based on Adaptive Scale Local Contrast Enhancement Combined with Visual Attention Mechanism[J]. Infrared Technology , 2019, 41(8): 764-771.
    [10]WANG Yu-xiang, HAN Zhen-duo, WANG Hong-min. Detection Algorithm for Dim Infrared Target Based on Multi-Difference Factor[J]. Infrared Technology , 2012, 34(6): 351-355. DOI: 10.3969/j.issn.1001-8891.2012.06.009

  • Cited by

    Periodical cited type(5)

    1. 李鹏. 基于红外测温技术的农村配网设备运行监测研究. 中国新技术新产品. 2025(03): 127-129 .
    2. 樊慧文. 深度学习在输变电设备故障状态检测中的应用研究. 电工技术. 2025(02): 95-97+101 .
    3. 刘传洋，吴一全. 基于红外图像的电力设备识别及发热故障诊断方法研究进展. 中国电机工程学报. 2025(06): 2171-2196 .
    4. 李冰，杜喜英，王玉莹，翟永杰. 基于改进YOLOv8n的变电设备红外图像实例分割算法. 电子测量技术. 2024(10): 151-159 .
    5. 佟忠正，孙旸子. 基于U-Net网络的电力设备巡检图像增强模型及其自动控制研究. 自动化与仪表. 2024(11): 79-82+91 .

    Other cited types(1)

Catalog

    Get Citation
    PDF
    XML
    Article views PDF downloads Cited by(6)
    Related

    Copyright © 《Infrared Technology》Editorial Office滇ICP备12000354号-3

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

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return