低空小型无人机空域冲突视觉感知技术研究进展

doi:10.7527/S1000-6893.2021.25645

综述

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

低空小型无人机空域冲突视觉感知技术研究进展

张洲宇¹, 曹云峰¹, 范彦铭²

1. 南京航空航天大学航天学院, 南京 210016;
2. 中航工业沈阳飞机设计研究所, 沈阳 110035

收稿日期:2021-04-12 修回日期:2021-08-17 出版日期:2022-08-15 发布日期:2021-08-17
通讯作者: 曹云峰,E-mail:cyfac@nuaa.edu.cn E-mail:cyfac@nuaa.edu.cn
基金资助:
国家自然科学基金(61673211);南京航空航天大学空间光电探测与感知工业和信息化部重点实验室开放课题(NJ2020021-01);中央高校基本科研业务费专项资金(NJ2020021)

Research progress of vision based aerospace conflict sensing technologies for small unmanned aerial vehicle in low altitude

ZHANG Zhouyu¹, CAO Yunfeng¹, FAN Yanming²

1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2. ACIC Shenyang Aircraft Design and Research Institute, Shenyang 110035, China

Received:2021-04-12 Revised:2021-08-17 Online:2022-08-15 Published:2021-08-17
Supported by:
National Natural Science Foundation of China (61673211);Open Project Funds for the Key Laboratory of Space Photoelectric Detection and Perception (Nanjing University of Aeronautics and Astronautics),Ministry of Industry and Information Technology (NJ2020021-01);Fundamental Research Funds for the Central Universities (NJ2020021)

摘要/Abstract

摘要： 无人机(UAV)空域冲突感知技术是国家空域集成中最具挑战的关键技术,针对小型无人机在低空空域应用愈加广泛、迫切需要融入低空空域的背景,综述了低空小型无人机空域冲突视觉感知技术的研究进展。首先,论述了低空小型无人机的应用现状,归纳了低空空域环境与小型无人机的典型特征;其次,根据感知对象的类型,将现有的空域冲突感知设备分为协同式与非协同式2类,通过对比总结了机器视觉作为低空小型无人机空域冲突感知设备的优势;然后,论述了视觉信息预处理、入侵目标视觉检测、基于视觉的避障路径规划这3项空域冲突视觉感知关键技术的最新研究进展;最后,总结了该领域有待进一步解决的难点,并对未来的发展方向进行展望。

关键词: 低空空域, 无人机(UVA), 空域冲突, 机器视觉, 感知与规避

Abstract: Unmanned Aerial Vehicles (UAV) aerospace conflict sensing is the most challenging key technology in national aerospace integration. Considering the broad application of small sized UAVs in low altitude aerospace, this paper makes a research survey of vision based aerospace conflict sensing technologies. Firstly, the application status of small sized UAVs in low altitude aerospace is briefly introduced, and the typical features of this kind of UAVs is summarized. Secondly, based on the categories of sensing objects, existing aerospace conflict sensing technologies are classified into cooperative sensing method and non-cooperative sensing method. The sensing technology most appropriate for small sized UAVs in low altitude aerospace is concluded. Then, the research progress of three key technologies of vision based aerospace conflict sensing, including visual information pre-processing, vision based intruder detection, vision based collision avoidance trajectory planning, are introduced. Finally, the challenges to be solved in this area are concluded, and several future research trends are introduced.

Key words: low altitude aerospace, Unmanned Aerial Vehicle (UAV), aerospace conflict, machine vision, sensing and avoidance

中图分类号:

V249.3
TP391

张洲宇, 曹云峰, 范彦铭. 低空小型无人机空域冲突视觉感知技术研究进展[J]. 航空学报, 2022, 43(8): 25645.

ZHANG Zhouyu, CAO Yunfeng, FAN Yanming. Research progress of vision based aerospace conflict sensing technologies for small unmanned aerial vehicle in low altitude[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(8): 25645.

参考文献

[1] United States Air Force. United States air force unmanned aircraft systems flight plan 2009-2047[S]. Washington, D.C.: United States Air Force, 2009.
[2] WARGO C A, CHURCH G C, GLANEUESKI J, et al. Unmanned Aircraft Systems (UAS) research and future analysis[C]//2014 IEEE Aerospace Conference. Piscataway: IEEE Press, 2014: 1-16.
[3] Radio Technical Commission for Aeronautics (RTCA). Operational and functional requirements and safety objectives (OFRSO) for unmanned aircraft systems (UAS) standards: DO-344[S]. Washington, D.C.: RTCA, 2013.
[4] 蔡志浩, 杨丽曼, 王英勋, 等. 无人机全空域飞行影响因素分析[J]. 北京航空航天大学学报, 2011, 37(2): 175-179, 184. CAI Z H, YANG L M, WANG Y X, et al. Analysis for whole airspace flight key factors of unmanned aerial vehicles[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(2): 175-179, 184 (in Chinese).
[5] 王英勋, 蔡志浩. 无人机的自主飞行控制[J]. 航空制造技术, 2009, 52(8): 26-31. WANG Y X, CAI Z H. Autonomous flight control of unmanned aerial vehicle[J]. Aeronautical Manufacturing Technology, 2009, 52(8): 26-31 (in Chinese).
[6] PRATS X, DELGADO L, RAMÍREZ J, et al. Requirements, issues, and challenges for sense and avoid in unmanned aircraft systems[J]. Journal of Aircraft, 2012, 49(3): 677-687.
[7] YU X, ZHANG Y M. Sense and avoid technologies with applications to unmanned aircraft systems: Review and prospects[J]. Progress in Aerospace Sciences, 2015, 74: 152-166.
[8] MCFADYEN A, MEJIAS L. A survey of autonomous vision-based see and avoid for unmanned aircraft systems[J]. Progress in Aerospace Sciences, 2016, 80: 1-17.
[9] WANG J, LIU Y, SONG H. Counter-unmanned aircraft system (s)(C-UAS): State of the art, challenges, and future trends[J]. IEEE Aerospace and Electronic Systems Magazine, 2021, 36(3): 4-29.
[10] 吕洋, 康童娜, 潘泉, 等. 无人机感知与规避: 概念、技术与系统[J]. 中国科学: 信息科学, 2019, 49(5): 520-537. LYU Y, KANG T N, PAN Q, et al. UAV sense and avoidance: Concepts, technologies, and systems[J]. Scientia Sinica (Informationis), 2019, 49(5): 520-537 (in Chinese).
[11] 曹云峰, 张洲宇, 钟佩仪, 等. 入侵目标视觉检测与识别的研究进展[J]. 计算机测量与控制, 2019, 27(8): 7-11. CAO Y F, ZHANG Z Y, ZHONG P Y, et al. Review on vision based intruder detection and recognition[J]. Computer Measurement & Control, 2019, 27(8): 7-11 (in Chinese).
[12] 张进, 胡明华, 张晨. 空中交通管理中的复杂性研究[J]. 航空学报, 2009, 30(11): 2132-2142. ZHANG J, HU M H, ZHANG C. Complexity research in air traffic management[J]. Acta Aeronautica et Astronautica Sinica, 2009, 30(11): 2132-2142 (in Chinese).
[13] 宫淑丽. 民航飞机电子系统[M]. 北京: 科学出版社, 2015. GONG S L. Electronic system of aviation plane[M]. Beijing: Science Press, 2015 (in Chinese).
[14] ANGELOV P. Sense and avoid in UAS[M]. Washington, D.C.: Wiley, 2012.
[15] 中国民航网. 莫桑比克航空客机与无人机发生相撞鼻锥受损[EB/OL]. (2017-01-07)[2021-04-05]. http://www.caacnews.com.cn/1/88/201701/t20170107_1208006_wap.html. China Civil Aviation Network. LAM Mozambique ailine collides with UAV and nose cone is damaged[EB/OL]. (2017-01-07)[2021-04-05].http://www.caacnews.com.cn/1/88/201701/t20170107_1208006_wap.html (in Chinese).
[16] ADMINISTRATION F A. Integration of civil unmanned aircraft systems (UAS) in the national airspace system (NAS) roadmap (first edition)[R]. Washington, D.C.: FAA, 2013.
[17] European RPAS Steering Group. Roadmap for the integration of civil remotely-piloted aircraft systems into the European Aviation System[R]. 2013.
[18] ADMINISTRATION F A. Integration of civil unmanned aircraft systems (UAS) in the national airspace system (NAS) roadmap (second edition)[R]. Washington, D.C.: FAA, 2018.
[19] MCFADYEN A, CLOTHIER R, CAMPBELL D, et al. Scoping study for remotely piloted aircraft systems integration into civil airspace[R]. 2014.
[20] BILLINGSLEY T B, KOCHENDERFER M J, CHRYSSANTHACOPOULOS J P. Collision avoidance for general aviation[J]. IEEE Aerospace and Electronic Systems Magazine, 2012, 27(7): 4-12.
[21] VALOVAGE E. Enhanced ADS-B research[J]. IEEE Aerospace and Electronic Systems Magazine, 2007, 22(5): 35-38.
[22] PATIAS P. Introduction to unmanned aircraft systems[J]. Photogrammetric Engineering & Remote Sensing, 2016, 82(2): 89-92.
[23] ROSEN P A, HENSLEY S, WHEELER K, et al. UAVSAR: New NASA airborne SAR system for research[J]. IEEE Aerospace and Electronic Systems Magazine, 2007, 22(11): 21-28.
[24] KARHOFF B C, LIMB J I, ORAVSKY S W, et al. Eyes in the domestic sky: An assessment of sense and avoid technology for the army’s "warrior" unmanned aerial vehicle[C]//2006 IEEE Systems and Information Engineering Design Symposium. Piscataway: IEEE Press, 2006: 36-42.
[25] OSBORNE III R W, BAR-SHALOM Y, WILLETT P, et al. Design of an adaptive passive collision warning system for UAVs[C]//SPIE Optical Engineering + Applications. Proc SPIE 7445, Signal and Data Processing of Small Targets 2009, 2009, 7445: 333-345.
[26] MEJIAS L, MCFADYEN A, FORD J J. Sense and avoid technology developments at Queensland University of Technology[J]. IEEE Aerospace and Electronic Systems Magazine, 2016, 31(7): 28-37.
[27] LAI J, MEJIAS L, FORD J J. Airborne vision-based collision-detection system[J]. Journal of Field Robotics, 2011, 28(2): 137-157.
[28] LAI J, FORD J J, MEJIAS L, et al. Characterization of sky-region morphological-temporal airborne collision detection[J]. Journal of Field Robotics, 2013, 30(2): 171-193.
[29] MOLLOY T L, FORD J J, MEJIAS L. Detection of aircraft below the horizon for vision-based detect and avoid in unmanned aircraft systems[J]. Journal of Field Robotics, 2017, 34(7): 1378-1391.
[30] LAI J, FORD J J, O'SHEA P, et al. Vision-based estimation of airborne target pseudobearing rate using hidden Markov model filters[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(4): 2129-2145.
[31] ZHANG Z Y, CAO Y F, DING M, et al. An intruder detection algorithm for vision based sense and avoid system[C]//2016 International Conference on Unmanned Aircraft Systems (ICUAS). Piscataway: IEEE Press, 2016: 550-556.
[32] ZHANG Z Y, CAO Y F, DING M, et al. Candidate regions extraction of intruder airplane under complex background for vision-based sense and avoid system[J]. IET Science, Measurement & Technology, 2017, 11(5): 571-580.
[33] CHEN R, GEVORKIAN A, FUNG A, et al. Multi-sensor data integration for autonomous sense and avoid[M]//Infotech@ Aerospace 2011. 2011: 1479.
[34] ZHANG Z Y, CAO Y F, ZHONG P Y, et al. An edge-boxes-based intruder detection algorithm for UAV sense and avoid system[J]. Transactions of Nanjing University of Aeronautics and Astronautics, 2019, 36(2): 253-263.
[35] LYU Y, PAN Q, ZHAO C H, et al. Autonomous stereo vision based collision avoid system for small UAV[C]//AIAA Information Systems-AIAA Infotech @ Aerospace. Reston: AIAA, 2017: 1150.
[36] LYU Y, PAN Q, ZHAO C H, et al. A UAV sense and avoid system design method based on software simulation[C]//2016 International Conference on Unmanned Aircraft Systems (ICUAS). Piscataway: IEEE Press, 2016: 572-579.
[37] LYU Y, PAN Q, ZHAO C H, et al. A vision based sense and avoid system for small unmanned helicopter[C]//2015 International Conference on Unmanned Aircraft Systems (ICUAS). Piscataway: IEEE Press, 2015: 586-592.
[38] CHO S, HUH S, SHIM D H, et al. Vision-based detection and tracking of airborne obstacles in a cluttered environment[J]. Journal of Intelligent & Robotic Systems, 2013, 69(1-4): 475-488.
[39] ZSEDROVITS T, BAUER P, HIBA A, et al. Performance analysis of camera rotation estimation algorithms in multi-sensor fusion for unmanned aircraft attitude estimation[J]. Journal of Intelligent & Robotic Systems, 2016, 84(1-4): 759-777.
[40] DING M, WEI L I. Sky-ground region segmentation and horizon detection using multi-scale dark channel images[J]. ICIC express letters. Part B, Applications: An International Journal of Research and Surveys, 2016, 7(2): 369-374.
[41] FASANO G, ACCADO D, MOCCIA A, et al. Sense and avoid for unmanned aircraft systems[J]. IEEE Aerospace and Electronic Systems Magazine, 2016, 31(11): 82-110.
[42] FASANO G, ACCARDO D, MOCCIA A, et al. Multi-sensor-based fully autonomous non-cooperative collision avoidance system for unmanned air vehicles[J]. Journal of Aerospace Computing, Information, and Communication, 2008, 5(10): 338-360.
[43] FASANO G, ACCARDO D, TIRRI A E, et al. Radar/electro-optical data fusion for non-cooperative UAS sense and avoid[J]. Aerospace Science and Technology, 2015, 46: 436-450.
[44] HOUGH V,PAUL C. Method and means for recognizing complex patterns: U.S. Patent 3,069,654[P]. 1962-12-18.
[45] DUDA R O, HART P E. Use of the Hough transformation to detect lines and curves in pictures[J]. Communications of the ACM, 1972, 15(1): 11-15.
[46] LIN Y C, PINTEA S L, VAN GEMERT J C. Deep Hough-transform line priors[M]//Computer Vision-ECCV 2020. Cham: Springer International Publishing, 2020: 323-340.
[47] LU X H, YAO J, LI K, et al. CannyLines: A parameter-free line segment detector[C]//2015 IEEE International Conference on Image Processing. Piscataway: IEEE Press, 2015: 507-511.
[48] SANTOS T, MOREIRA M, ALMEIDA J, et al. PLineD: Vision-based power lines detection for unmanned aerial vehicles[C]//2017 IEEE International Conference on Autonomous Robot Systems and Competitions. Piscataway: IEEE Press, 2017: 253-259.
[49] VON GIOI R G, JAKUBOWICZ J, MOREL J M, et al. LSD: A fast line segment detector with a false detection control[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(4): 722-732.
[50] OSSIMITZ C, TAHERINEJAD N. A fast line segment detector using approximate computing[C]//2021 IEEE International Symposium on Circuits and Systems. Piscataway: IEEE Press, 2021: 1-5.
[51] JIANG X Y, MA J Y, XIAO G B, et al. A review of multimodal image matching: Methods and applications[J]. Information Fusion, 2021, 73: 22-71.
[52] ATARITA F. Hyperspectral imaging simulator and applications for unmanned aerial vehicles[D]. Kingston: Queen’s University, 2021.
[53] LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[54] JHAN J P, RAU J Y. A generalized tool for accurate and efficient image registration of UAV multi-lens multispectral cameras by N-SURF matching[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 6353-6362.
[55] CUCKA P, ROSENFELD A. Linear feature compatibility for pattern-matching relaxation[J]. Pattern Recognition, 1992, 25(2): 189-196.
[56] BENTOUTOU Y, TALEB N, KPALMA K, et al. An automatic image registration for applications in remote sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(9): 2127-2137.
[57] HAN X F, LEUNG T, JIA Y Q, et al. MatchNet: Unifying feature and metric learning for patch-based matching[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2015: 3279-3286.
[58] ZAGORUYKO S, KOMODAKIS N. Deep compare: A study on using convolutional neural networks to compare image patches[J]. Computer Vision and Image Understanding, 2017, 164: 38-55.
[59] MA J Y, JIANG X Y, FAN A X, et al. Image matching from handcrafted to deep features: A survey[J]. International Journal of Computer Vision, 2021, 129(1): 23-79.
[60] DING M, WEI L, WANG B F. Research on fusion method for infrared and visible images via compressive sensing[J]. Infrared Physics & Technology, 2013, 57: 56-67.
[61] PAJARES G, MANUEL DE LA CRUZ J. A wavelet-based image fusion tutorial[J]. Pattern Recognition, 2004, 37(9): 1855-1872.
[62] DU J, LI W S, XIAO B, et al. Union Laplacian pyramid with multiple features for medical image fusion[J]. Neurocomputing, 2016, 194: 326-339.
[63] YADAV S P, YADAV S. Image fusion using hybrid methods in multimodality medical images[J]. Medical & Biological Engineering & Computing, 2020, 58(4): 669-687.
[64] ZHANG Q, LIU Y, BLUM R S, et al. Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review[J]. Information Fusion, 2018, 40: 57-75.
[65] JIN X, JIANG Q, YAO S W, et al. A survey of infrared and visual image fusion methods[J]. Infrared Physics & Technology, 2017, 85: 478-501.
[66] ZHU Z Q, YIN H P, CHAI Y, et al. A novel multi-modality image fusion method based on image decomposition and sparse representation[J]. Information Sciences, 2018, 432: 516-529.
[67] LIU C H, QI Y, DING W R. Infrared and visible image fusion method based on saliency detection in sparse domain[J]. Infrared Physics & Technology, 2017, 83: 94-102.
[68] LIU Y, CHEN X, WANG Z F, et al. Deep learning for pixel-level image fusion: Recent advances and future prospects[J]. Information Fusion, 2018, 42: 158-173.
[69] MA J Y, LIANG P W, YU W, et al. Infrared and visible image fusion via detail preserving adversarial learning[J]. Information Fusion, 2020, 54: 85-98.
[70] MA J Y, YU W, LIANG P W, et al. FusionGAN: A generative adversarial network for infrared and visible image fusion[J]. Information Fusion, 2019, 48: 11-26.
[71] LI H, WU X J. DenseFuse: A fusion approach to infrared and visible images[J]. IEEE Transactions on Image Processing, 2019, 28(5): 2614-2623.
[72] LI H, WU X J, KITTLER J. Infrared and visible image fusion using a deep learning framework[C]//2018 24th International Conference on Pattern Recognition (ICPR). Piscataway: IEEE Press, 2018: 2705-2710.
[73] YE Q, LI L G, TAN L, et al. Image fusion based on convolution sparse representation and pulse coupled neural network in non-subsampled contourlet domain[J]. International Journal of Embedded Systems, 2020, 12(1): 102-104.
[74] TAO J, CAO Y F, DING M, et al. Visible and infrared image fusion for space debris recognition with convolutional sparse representaiton[C]//2018 IEEE CSAA Guidance, Navigation and Control Conference. Piscataway: IEEE Press, 2018: 1-5.
[75] SIVARAMAN S, TRIVEDI M M. Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(4): 1773-1795.
[76] MUKHTAR A, XIA L K, TANG T B. Vehicle detection techniques for collision avoidance systems: A review[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(5): 2318-2338.
[77] KARASEV V, AYVACI A, HEISELE B, et al. Intent-aware long-term prediction of pedestrian motion[C]//2016 IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2016: 2543-2549.
[78] KELLER C G, GAVRILA D M. Will the pedestrian cross? A study on pedestrian path prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(2): 494-506.
[79] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2005: 886-893.
[80] CLARK M, KERN Z, PRAZENICA R J. A vision-based proportional navigation guidance law for UAS sense and avoid[C]//AIAA Guidance, Navigation, and Control Conference. Reston: AIAA, 2015: 0074.
[81] VANEK B, PENI T, BOKOR J, et al. Performance analysis of a vision only sense and avoid system for small UAVs[C]//AIAA Guidance, Navigation, and Control Conference. Reston: AIAA, 2011: 6602.
[82] ROZANTSEV A, LEPETIT V, FUA P. Detecting flying objects using a single moving camera[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(5): 879-892.
[83] OKSUZ K, CAM B C, KALKAN S, et al. Imbalance problems in object detection: A review[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(10): 3388-3415.
[84] PADILLA R, NETTO S L, DA SILVA E A B. A survey on performance metrics for object-detection algorithms[C]//2020 International Conference on Systems, Signals and Image Processing (IWSSIP). Piscataway: IEEE Press, 2020: 237-242.
[85] ZITNICK C L, DOLLÁR P. Edge boxes: Iocating object proposals from edges[C]//Computer Vision-ECCV 2014, 2014.
[86] UIJLINGS J R R, SANDE K, GEVERS T, et al. Selective search for object recognition[J]. International Journal of Computer Vision, 2013, 104(2): 154-171.
[87] AHONEN T, HADID A, PIETIKÄINEN M. Face description with local binary patterns: Application to face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(12): 2037-2041.
[88] ANBARASU B, ANITHA G. Indoor scene recognition for micro aerial vehicles navigation using enhanced SIFT-ScSPM descriptors[J]. Journal of Navigation, 2020, 73(1): 37-55.
[89] CAO X B, WU C X, YAN P K, et al. Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos[C]//2011 18th IEEE International Conference on Image Processing. Piscataway: IEEE Press, 2011: 2421-2424.
[90] DHILLON A, VERMA G K. Convolutional neural network: A review of models, methodologies and applications to object detection[J]. Progress in Artificial Intelligence, 2020, 9(2): 85-112.
[91] TONG K, WU Y Q, ZHOU F. Recent advances in small object detection based on deep learning: A review[J]. Image and Vision Computing, 2020, 97: 103910.
[92] SHARMA V, MIR R N. A comprehensive and systematic look up into deep learning based object detection techniques: A review[J]. Computer Science Review, 2020, 38: 100301.
[93] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2016: 779-788.
[94] REDMON J, FARHADI A. YOLO9000: Better, faster, stronger[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2017: 6517-6525.
[95] REDMON J, FARHADI A. YOLOv3: An incremental improvement[EB/OL].arXiv Preprint: 1804.02767,2018.
[96] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: Optimal speed and accuracy of object detection[DB/OL]. arXiv preprint: 2004.10934, 2020.
[97] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2014: 580-587.
[98] GIRSHICK R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2015: 1440-1448.
[99] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[100] GARCIA-GARCIA B, BOUWMANS T, ROSALES SILVA A J. Background subtraction in real applications: Challenges, current models and future directions[J]. Computer Science Review, 2020, 35: 100204.
[101] AGRAWAL S, NATU P. Segmentation of moving objects using numerous background subtraction methods for surveillance applications[J]. International Journal of Innovative Technology and Exploring Engineering, 2020, 9(3): 2553-2563.
[102] ANGADI S, NANDYAL S. A review on object detection and tracking in video surveillance[J]. International Journal of Advanced Research in Engineering and Technology, 2020, 11(9):1033-1042.
[103] TOM A J, GEORGE S N. Simultaneous reconstruction and moving object detection from compressive sampled surveillance videos[J]. IEEE Transactions on Image Processing, 2020, 29: 7590-7602.
[104] WANG Y L, WEI H C, DING X Y, et al. Video background/foreground separation model based on non-convex rank approximation RPCA and superpixel motion detection[J]. IEEE Access, 2020, 8: 157493-157503.
[105] ZHANG Z Y, CAO Y F, DING M, et al. Spatial and temporal context information fusion based flying objects detection for autonomous sense and avoid[C]//2018 International Conference on Unmanned Aircraft Systems (ICUAS). Piscataway: IEEE Press, 2018: 569-578.
[106] LIU P P, LYU M, KING I, et al. SelFlow: Self-supervised learning of optical flow[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2019: 4566-4575.
[107] DE CROON G C H E, DE WAGTER C, SEIDL T. Enhancing optical-flow-based control by learning visual appearance cues for flying robots[J]. Nature Machine Intelligence, 2021, 3(1): 33-41.
[108] LIAO B, HU J L, GILMORE R O. Optical flow estimation combining with illumination adjustment and edge refinement in livestock UAV videos[J]. Computers and Electronics in Agriculture, 2021, 180: 105910.
[109] ZHANG Z Y, CAO Y F, DING M, et al. Monocular vision based obstacle avoidance trajectory planning for unmanned aerial vehicle[J]. Aerospace Science and Technology, 2020, 106: 106199.
[110] KNYAZ V A, KNIAZ V V, REMONDINO F, et al. 3D reconstruction of a complex grid structure combining UAS images and deep learning[J]. Remote Sensing, 2020, 12(19): 3128.
[111] ZHENG T X, HUANG S, LI Y F, et al. Key techniques for vision based 3D reconstruction: A review[J]. Acta Automatica Sinica, 2020, 46(4): 631-652.
[112] INGALE A K, DIVYA U J. Real-time 3D reconstruction techniques applied in dynamic scenes: A systematic literature review[J]. Computer Science Review, 2021, 39: 100338.
[113] WU F P, ZHU S K, YE W L. A single image 3D reconstruction method based on a novel monocular vision system[J]. Sensors, 2020, 20(24): 7045.
[114] FU K, PENG J S, HE Q W, et al. Single image 3D object reconstruction based on deep learning: A review[J]. Multimedia Tools and Applications, 2021, 80(1): 463-498.
[115] SAPUTRA M R U, MARKHAM A, TRIGONI N. Visual SLAM and structure from motion in dynamic environments[J]. ACM Computing Surveys, 2019, 51(2): 1-36.
[116] PASQUALETTO CASSINIS L, FONOD R, GILL E. Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft[J]. Progress in Aerospace Sciences, 2019, 110: 100548.
[117] LU X X. A review of solutions for perspective-n-point problem in camera pose estimation[J]. Journal of Physics: Conference Series, 2018, 1087: 052009.
[118] KIM P, LEE H, KIM H J. Autonomous flight with robust visual odometry under dynamic lighting conditions[J]. Autonomous Robots, 2019, 43(6): 1605-1622.
[119] KUO X Y, LIU C E, LIN K C, et al. Dynamic attention-based visual odometry[C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway: IEEE Press, 2021: 5753-5760.
[120] PARAMESHWARA C M, SANKET N J, SINGH C D, et al. 0-MMS: Zero-shot multi-motion segmentation with A monocular event camera[C]//2021 IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2021: 9594-9600.
[121] LAGA H, JOSPIN L V, BOUSSAID F, et al. A survey on deep learning techniques for stereo-based depth estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(4): 1738-1764.
[122] MARR D, POGGIO T. A computational theory of human stereo vision[J]. Proceedings of the Royal Society of London Series B, Biological Sciences, 1979, 204(1156): 301-328.
[123] ZOU X J, ZOU H X, LU J. Virtual manipulator-based binocular stereo vision positioning system and errors modelling[J]. Machine Vision and Applications, 2012, 23(1): 43-63.
[124] 李占贤, 许哲. 双目视觉的成像模型分析[J]. 机械工程与自动化, 2014(4): 191-192. LI Z X, XU Z. Analysis of imaging model of binocular vision[J]. Mechanical Engineering & Automation, 2014(4): 191-192 (in Chinese).
[125] WANG Q, MENG Z J, LIU H. Review on application of binocular vision technology in field obstacle detection[J]. IOP Conference Series: Materials Science and Engineering, 2020, 806(1): 012025.
[126] FAN X J, GUO Y J, LIU H, et al. Improved artificial potential field method applied for AUV path planning[J]. Mathematical Problems in Engineering, 2020, 2020: 6523158.
[127] PARK S O, LEE M C, KIM J. Trajectory planning with collision avoidance for redundant robots using Jacobian and artificial potential field-based real-time inverse kinematics[J]. International Journal of Control, Automation and Systems, 2020, 18(8): 2095-2107.
[128] WANG D Y, WANG P, ZHANG X T, et al. An obstacle avoidance strategy for the wave glider based on the improved artificial potential field and collision prediction model[J]. Ocean Engineering, 2020, 206: 107356.
[129] LAYMAN T, FIELDS T, YAKIMENKO O A. Evaluation of proportional navigation for multirotor pursuit[C]//AIAA Scitech 2021 Forum. Reston: AIAA, 2021: 1813.
[130] BAUER P, HIBA A, BOKOR J, et al. Three dimensional intruder closest point of approach estimation based-on monocular image parameters in aircraft sense and avoid[J]. Journal of Intelligent & Robotic Systems, 2019, 93(1-2): 261-276.
[131] BAUER P, HIBA A, BOKOR J. Monocular image-based intruder direction estimation at closest point of approach[C]//2017 International Conference on Unmanned Aircraft Systems (ICUAS). Piscataway: IEEE Press, 2017: 1108-1117.
[132] TAN C Y, HUANG S N, TAN K K, et al. Collision avoidance design on unmanned aerial vehicle in 3D space[J]. Unmanned Systems, 2018, 6(4): 277-295.
[133] LEVINE S. Reinforcement learning and control as probabilistic inference: Tutorial and review[DB/OL]. ArXiv preprint: 1805.00909, 2018.
[134] KIRAN B R, SOBH I, TALPAERT V, et al. Deep reinforcement learning for autonomous driving: A survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2021: 1-18.
[135] VAN DEN BERG J, LIN M, MANOCHA D. Reciprocal Velocity Obstacles for real-time multi-agent navigation[C]//2008 IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2008: 1928-1935.
[136] CHEN Y F, LIU M, EVERETT M, et al. Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning[C]//2017 IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2017: 285-292.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

低空小型无人机空域冲突视觉感知技术研究进展

Research progress of vision based aerospace conflict sensing technologies for small unmanned aerial vehicle in low altitude

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 13

编辑推荐

Metrics

本文评价

[1]	吴启晖, 董超, 贾子晔, 崔灿, 冯斯梦, 周福辉, 谢华. 低空智联网组网与控制理论方法[J]. 航空学报, 2024, 45(3): 28809-028809.
[2]	廖小罕, 屈文秋, 徐晨晨, 贺洪波, 王俊伟, 石伟博. 城市空中交通及其新型基础设施低空公共航路研究综述[J]. 航空学报, 2023, 44(24): 28521-028521-.
[3]	张毅, 张焱, 张宇, 张勇, 刘荻. 基于多级特征增强融合的红外飞机目标检测方法[J]. 航空学报, 2023, 44(22): 629220-629220.
[4]	杜天宇, 王珉, 陈文亮. 基于单目视觉的多类型装配基准稳健检测方法[J]. 航空学报, 2023, 44(12): 427852-427852.
[5]	刘芳, 孙亚楠. 基于自适应融合网络的无人机目标跟踪算法[J]. 航空学报, 2022, 43(7): 325522-325522.
[6]	刘占, 张俊, 尹佳, 赵万华. 基于机器视觉的立铣刀几何与状态参数在机检测[J]. 航空学报, 2022, 43(7): 425593-425593.
[7]	康硕, 柯臻铮, 王璇, 朱伟东. 基于红外和可见光图像融合的铺丝缺陷检测方法[J]. 航空学报, 2022, 43(3): 425187-425187.
[8]	窦建宇, 潘翀. 基于神经网络的体视PIV空间标定模型[J]. 航空学报, 2021, 42(4): 524720-524720.
[9]	管祥民, 吕人力. 基于满意博弈论的复杂低空飞行冲突解脱方法[J]. 航空学报, 2017, 38(S1): 721475-721475.
[10]	陈唯实, 李敬. 基于空域特性的低空空域雷达目标检测[J]. 航空学报, 2015, 36(9): 3060-3068.
[11]	温卓漫, 王延杰, 初广丽, 金明河. 空间站机械臂位姿测量中合作靶标的快速识别[J]. 航空学报, 2015, 36(4): 1330-1338.
[12]	陆宇平, 杨朝星, 刘洋洋. 空中加油系统的建模与控制技术综述[J]. 航空学报, 2014, 35(9): 2375-2389.
[13]	于合龙, 苏恒强, 汪岩, 冯雪. SUSAN角点检测和匹配算法在高温变形测量中的应用[J]. 航空学报, 2013, 34(5): 1064-1072.