[1]ZHU Qidan,LI Xiaotong,ZHENG Tianhao.Research on real-time vision measurement algorithm of shipborne aircraft pose[J].CAAI Transactions on Intelligent Systems,2021,16(6):1045-1055.[doi:10.11992/tis.202103014]
Copy

Research on real-time vision measurement algorithm of shipborne aircraft pose

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 6 Page number: 1045-1055 Column: 学术论文—机器感知与模式识别 Public date: 2021-11-05
Title:
Research on real-time vision measurement algorithm of shipborne aircraft pose
Author(s):
ZHU Qidan;  LI Xiaotong;  ZHENG Tianhao
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
machine vision; shipborne aircraft pose; deep learning; target detection; edge extraction; wireframe matching; GPU acceleration; target segmentation
CLC:
TP391.4
DOI:
10.11992/tis.202103014
Abstract:
Real-time detection of carrier aircraft pose is of great significance to the movement control, trajectory planning, and collision avoidance of carrier-based aircraft on deck. The traditional scheduling of carrier aircraft mainly relies on the manual judgment of the position and heading angle of the carrier-based aircraft. Traditional methods fail to obtain accurate data and readily cause collisions because of operator negligence and fatigue. Therefore, a real-time visual measurement algorithm for the pose of carrier aircraft is proposed. First, the carrier aircraft is identified and segmented based on the you only look once version 4 (YOLO-V4) network and the Canny edge extraction algorithm. Then, an innovative algorithm for wireframe template matching is proposed, and the best pose is obtained by calculating the matching degree between the contour of the carrier aircraft and the wireframe template. The algorithm meets the real-time requirements through parallelization and graphics processing unit acceleration, and the test is completed in the 1:70 and 1:14 physical simulation environments. The results show that the recognition rate of this algorithm is >95%, the accuracy is within 0.7° and 8 mm, and the speed can reach 8 Hz.
References:
[1] 于鲲, 丛明煜, 戴文聪. 基于三维模型的空间目标视觉位姿测量[J]. 仪器仪表学报, 2019, 40(4): 179-188
YU Kun, CONG Mingyu, DAI Wencong. Spatial target vision pose measurement based on 3D model[J]. Chinese journal of scientific instrument, 2019, 40(4): 179-188
[2] 季涛, 李福东, 杨月全, 等. 基于改进ICP算法和CAD模型库的单目视觉位姿测量[J]. 扬州大学学报(自然科学版), 2019, 22(1): 50-54, 61
JI Tao, LI Fudong, YANG Yuequan, et al. Monocular vision pose measurement based on improved ICP algorithm and CAD model[J]. Journal of Yangzhou University (natural science edition), 2019, 22(1): 50-54, 61
[3] 刘祥. 基于双目视觉的空间非合作目标位姿测量方法[D]. 哈尔滨: 哈尔滨工业大学, 2019: 1-8.
LIU Xiang. Non-cooperative target pose estimation based on stereo vision[D]. Harbin: Harbin Institute of Technology, 2019: 1-8.
[4] 王晓林, 苏松志, 刘晓颖, 等. 一种基于级联神经网络的飞机检测方法[J]. 智能系统学报, 2020, 15(4): 697-704
WANG Xiaolin, SU Songzhi, LIU Xiaoying, et al. Cascade convolutional neural networks for airplane detection[J]. CAAI transactions on intelligent systems, 2020, 15(4): 697-704
[5] 王婷婷, 苍岩, 毕晓君, 等. 改进的Faster R-CNN网络预测尺度参数[J]. 哈尔滨工程大学学报, 2021, 42(3): 426-432
WANG Tingting, CANG Yan, BI Xiaojun, et al. Improved faster R-CNN algorithm for object parameter prediction[J]. Journal of Harbin Engineering University, 2021, 42(3): 426-432
[6] WANG Chen, XU Danfei, ZHU Yuke, et al. DenseFusion: 6D object pose estimation by iterative dense fusion[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA, 2019: 3343-3352.
[7] TEKIN B, SINHA S N, FUA P. Real-time seamless single shot 6D object pose prediction[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018: 1-2.
[8] 李少飞, 史泽林, 庄春刚. 基于物体点云6D位姿估计的深度学习方法[EB/OL]. [2021-02-19] https://doi.org/10.19678/j.issn.1000-3428.0058768.
LI Shaofei, SHI Zelin, ZHUANG Chungang. Deep learning based 6D object pose estimation using point clouds[EB/OL]. [2021-02-19] https://doi.org/10.19678/j.issn.1000-3428.0058768.
[9] TIAN Meng, ANG JR M H, LEE G H. Shape prior deformation for categorical 6D object pose and size estimation[C]//Proceedings of the 16th European Conference on Computer Vision. Glasgow, UK, 2020: 1-2.
[10] 赵永强, 饶元, 董世鹏, 等. 深度学习目标检测方法综述[J]. 中国图象图形学报, 2020, 25(4): 629-654
ZHAO Yongqiang, RAO Yuan, DONG Shipeng, et al. Survey on deep learning object detection[J]. Chinese journal of image and graphics, 2020, 25(4): 629-654
[11] 李同, 阮士峰, 陈卓, 等. 基于卷积神经网络的目标检测综述[J]. 科技经济导刊, 2020, 28(27): 18-20
LI Tong, RUAN Shifeng, CHEN Zhuo, et al. Overview of target detection based on convolutional neural networks[J]. Technology and economic guide, 2020, 28(27): 18-20
[12] HEI Law, JIA Deng. Cornernet: detecting objects as paired keypoints [C].//Proceedings of the European Conference on Computer Vision (ECCV). Munich, Germany, 2018: 765-781.
[13] ZHU C, HE Y, SAVVIDES M. Feature selective anchor-free module for single-shot object detection [C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, USA,2019: 840-849.
[14] ZHOU Xingyi, ZHOU Jiacheng, KR?HENBüHL P. Bottom-up object detection by grouping extreme and center points[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA, 2019: 850-859.
[15] TIAN Z, SHEN C, CHEN H, et al. FCOS: fully convolutional one-stage object detection [C].//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul, Korea (South), 2019: 9626-9635.
[16] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. Yolov4: Optimal speed and accuracy of object detection[EB/OL]. (2020-04-22)[2021-05-01]. https://arxiv.org/pdf/2004.10934.pdf.
[17] 曹田, 李勃, 任福继, 等. 快速的圆投影图像匹配算法[J]. 智能系统学报, 2020, 15(1): 84-91
CAO Tian, LI Bo, REN Fuji, et al. Fast image matching algorithm based on circular projection[J]. CAAI transactions on intelligent systems, 2020, 15(1): 84-91
[18] 吴清辉. 基于改进SURF特征点的模板匹配算法[J]. 机电工程技术, 2021, 50(6): 208-211
WU Qinghui. Template matching algorithm based on improved SURF feature points[J]. Mechanical & electrical engineering technology, 2021, 50(6): 208-211
[19] 江苏蓬, 向伟, 刘云鹏, 等. 采用多特征共生矩阵的模板匹配[J]. 光学 精密工程, 2021, 29(6): 1459-1467
JIANG Supeng, XIANG Wei, LIU Yunpeng, et al. Template matching algorithm with multi-feature co-occurrence matrix[J]. Optics and precision engineering, 2021, 29(6): 1459-1467
[20] 王丽芳, 吕建平. 小波变换和Canny算子相结合的图像边缘检测算法[J]. 计算机与数字工程, 2014, 42(8): 1480-1483
WANG Lifang, LV Jianping. Image segmentation method based on wavelet transform and Canny operator[J]. Computer and digital engineering, 2014, 42(8): 1480-1483
[21] 张月圆, 曾庆化, 刘建业, 等. 基于Canny的改进图像边缘检测算法[J]. 导航与控制, 2019, 18(1): 84-90
ZHANG Yueyuan, ZENG Qinghua, LIU Jianye, et al. An Improved image edge detection algorithm based on Canny algorithm[J]. Navigation and control, 2019, 18(1): 84-90
[22] 杨福嘉, 郑丽颖. 基于SIFT的新特征提取匹配算法[J]. 应用科技, 2019, 46(2): 94-97, 103
YANG Fujia, ZHENG Liying. New feature extraction matching algorithm based on SIFT[J]. Applied science and technology, 2019, 46(2): 94-97, 103
[23] DAVIS B J, KASZETA R W, CHAMBERS R D, et al. Machine vision tracking of carrier-deck assets for improved launch safety[C]. Proceedings of Signal Processing, Sensor Fusion, and Target Recognition. Baltimore, United States, 2013: 1-11.
[24] 丁科, 谭营. GPU 通用计算及其在计算智能领域的应用[J]. 智能系统学报, 2015, 10(1): 1-11
DING Ke, TAN Ying. A review on general purpose computing on GPUs and its applications in computation-al intelligence[J]. CAAI transactions on intelligent sys-tems, 2015, 10(1): 1-11
[25] 张毅, 沙建松. 基于图优化的移动机器人视觉SLAM[J]. 智能系统学报, 2018, 13(2): 290-295
ZHANG Yi, SHA Jiansong. Visual-SLAM for mobile robot based on graph optimization[J]. CAAI transactions on intelligent systems, 2018, 13(2): 290-295
[26] 李占旗, 高继东, 彭凯, 等. 基于PnP算法的前方车辆测距方法[J]. 国外电子测量技术, 2020, 39(12): 104-108
LI Zhanqi, GAO Jidong, PENG Kai, et al. Method of measurement vehicle distance based on PnP[J]. Foreign electronic measurement technology, 2020, 39(12): 104-108
Similar References:

Memo

-

Last Update: 2021-12-25

Copyright © CAAI Transactions on Intelligent Systems