Advanced Search
中文
Volume 44 Issue 11
Nov.  2022
Turn off MathJax
Article Contents
Abstract
References
Related Articles
XIA Yan. Research on 3D Target Recognition Algorithm Based on Infrared Features[J]. Infrared Technology , 2022, 44(11): 1161-1166.
Citation: XIA Yan. Research on 3D Target Recognition Algorithm Based on Infrared Features[J]. Infrared Technology , 2022, 44(11): 1161-1166.
shu

Research on 3D Target Recognition Algorithm Based on Infrared Features

  • College of Information, Changchun Polytechnic, Changchun 130033, China

More Information
  • Received Date: April 11, 2022
  • Revised Date: July 27, 2022
    Graphical Abstract
    • Abstract
  • Target recognition based on 3D features has problems, such as easy misjudgment in similar point cloud domains and large amounts of total data computation, which results in a low target detection rate and high misjudgment rate. To improve the accuracy and speed of target recognition, a three-dimensional target recognition algorithm based on infrared features was proposed. The system simultaneously obtains the 2D infrared image and 3D point cloud data of the target area, obtains the projection range of the target using the salient features of the target's infrared characteristics, and calculates the pose relationship between the system and the target. The limited range of the target in the point cloud data is calculated according to the infrared feature mapping relationship, thereby significantly reducing the total number of point clouds that need to be matched and calculated. In the experiment, the same target vehicle was tested under the same background conditions, and the recognition data for three different test angles were recorded and analyzed. The obtained results indicated that the average target detection rate of the conventional point cloud recognition algorithm, average false positive rate, and convergence time were 93.4%, 19.5%, and 4.77 s, respectively. In addition, the average target detection rate of this algorithm, average false positive rate, and convergence time were 98.7%, 1.5%, and 1.23 s, respectively. It can be inferred that the detection and misjudgment rates of the target recognition algorithm based on infrared features are more advantageous, and the processing speed is faster.
    • FullText(HTML)
    • References (12)
  • [1]
    REN S, HE K, Girshick R, et al. Faster R-CNN: Real-time target detection in regional planning Network[J]. IEEE Pair Model Analysis and Machinery Information, 2017, 39(6): 1137-1149.
    [2]
    GUO Y L, Sohel F, Bennamoun M, et al. A novel local surface feature for 3D object recognition under clutter and occlusion[J]. Information Sciences, 2015, 293: 196-213. DOI: 10.1016/j.ins.2014.09.015
    [3]
    王果, 王成, 张振鑫, 等. 利用车载激光点云的分车带识别及单木分割方法[J]. 激光与红外, 2020, 50(11): 1333-1337. DOI: 10.3969/j.issn.1001-5078.2020.11.008

    WANG Guo, WANG Cheng, ZHANG Zhenxin, et al. Single tree segmentation method of urban distributing belt based on vehicle-borne laser point cloud data[J]. Laser & Infrared, 2020, 50(11): 1333-1337. DOI: 10.3969/j.issn.1001-5078.2020.11.008
    [4]
    胡海瑛, 惠振阳, 李娜. 基于多基元特征向量融合的机载LiDAR点云分类[J]. 中国激光, 2020, 47(8): 229-239. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ202008029.htm

    HU Haiying, HUI Zhenyang, LI Na. Airborne LiDAR point cloud classification based on multiple-entity eigenvector fusion[J]. Chinese Journal of Lasers, 2020, 47(8): 229-239. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ202008029.htm
    [5]
    Sochor J, Spaňhel J, Herout A. Boxcars: improving fine-grained recognition of vehicles using 3-d bounding boxes in traffic surveillance[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 20(1): 97-108.
    [6]
    Sochor J, Herout A, Havel J. BoxCars: 3D boxes as CNN input for improved fine-grained vehicle recognition[C]// IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 3006-3015.
    [7]
    Dubská M, Herout A, Juránek R, et al. Fully automatic roadside camera calibration for traffic surveillance[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 16(3): 1162-1171.
    [8]
    Dubská M, Herout A, Sochor J. Automatic camera calibration for traffic understanding[C]// Proceedings of the British Machine Vision Conference(BMVC), 2014: 1-12.
    [9]
    GISEOK K, JAE- SOO C. Vision- Based vehicle detection and inter- vehicle distance estimation for driver alarm system[J]. Optical Review, 2012, 25(6): 388- 393.
    [10]
    薛培林, 吴愿, 殷国栋, 等. 基于信息融合的城市自主车辆实时目标识别[J]. 机械工程学报, 2020, 56(12): 165-173. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202012021.htm

    XUE Peilin, WU Yuan, YIN Guodong, et al. Real-time target recognition for urban autonomous vehicles based on information fusion[J]. Journal of Mechanical Engineering, 2020, 56(12): 165-173. https://www.cnki.com.cn/Article/CJFDTOTAL-JXXB202012021.htm
    [11]
    仝选悦, 吴冉, 杨新锋, 等. 红外与激光融合目标识别方法[J]. 红外与激光工程, 2018, 47(5): 158-165. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201805025.htm

    TONG Xuanyue, WU Ran, YANG Xinfeng, et al. Fusion target recognition method of infrared and laser[J]. Infrared and Laser Engineering, 2018, 47(5): 158-165. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ201805025.htm
    [12]
    YAN Y, MAO Y, LI B. Second: sparsely embedded convolutional detection[J]. Sensors, 2018, 18(10): 3337/1-17.
    • Related Articles

  • Related Articles

    [1]LI Xianjing, HAO Zhenghui. Infrared Thermal Imaging Smoke Detection Based on Motion and Fuzzy Features[J]. Infrared Technology , 2024, 46(3): 325-331.
    [2]ZHENG Kai, LUO Zhitao, ZHANG Hui. Research Status of Infrared Thermography in NDT of FRP Composites/Thermal Barrier Coatings and Its Development[J]. Infrared Technology , 2023, 45(10): 1008-1019.
    [3]GONG Jiamin, WU Yijie, LIU Fang, ZHANG Yunsheng, LEI Shutao, ZHU Zehao. Image Fusion Algorithm Based on Improved Fuzzy C-means Clustering[J]. Infrared Technology , 2023, 45(8): 849-857.
    [4]JIN Meixiu, ZHU Shihu, WANG Tong, ZHUANG Feifei. Nondestructive Crack Testing via Infrared Thermal Imaging Using Halogen Lamp Excitation[J]. Infrared Technology , 2022, 44(4): 421-427.
    [5]ZHANG Qingyu, FAN Yugang, GAO Yang. Defect Detection of Eddy-Current Thermography Based on Single-Scale Retinex and Improved K-means Clustering[J]. Infrared Technology , 2020, 42(10): 1001-1006.
    [6]KONG Songtao, HUANG Zhen, YANG Jinru. Research Status and Development of Image Processing for Infrared Thermal Image Nondestructive Testing[J]. Infrared Technology , 2019, 41(12): 1133-1140.
    [7]ZHENG Kai, JIANG Haijun, CHEN Li. Infrared Thermography NDT and Its Development[J]. Infrared Technology , 2018, 40(5): 401-411.
    [8]Numerical Simulation of Lock-in Thermograpy for Infrared Nondestructive Testing[J]. Infrared Technology , 2013, (2): 119-122.
    [9]ZHAO Jing-yuan, WANG Li-ming, LIU Bin. The Finite Element Simulation and Analysis of the Infrared NDT for Inner Defects in Casting Product[J]. Infrared Technology , 2008, 30(7): 429-432. DOI: 10.3969/j.issn.1001-8891.2008.07.016
    [10]XIE Xing-sheng, YAN Fang, LU Jia-jia, YE Yu-tang, DENG Jun-jie, WEI Jian-ying, SUN Guo-dong, FANG Liang. The Applications of Thermal Wave NDT in Turbine Blades Testing[J]. Infrared Technology , 2007, 29(9): 552-555. DOI: 10.3969/j.issn.1001-8891.2007.09.015

  • Cited by

    Periodical cited type(3)

    1. 王茜萌. 基于行为聚类的电子商务恶意支付用户检测. 信息与电脑(理论版). 2023(03): 25-27 .
    2. 杜玉红，张松奇. 基于红外图像的耐腐蚀船舶材料表面缺陷识别研究. 舰船科学技术. 2023(14): 152-155 .
    3. 苗勃. 基于红外图像增强算法的石油储罐内油品温度过高风险自动识别方法. 化工自动化及仪表. 2023(06): 900-904 .

    Other cited types(10)

Catalog

    Get Citation
    PDF
    XML
    Article views (164) PDF downloads (46) Cited by(13)
    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