Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/133896| 題名: | 微型無人機基於視覺自動區域偵查與物件偵測定位 Miniature UAV based on visual automatic area search with object detection and geolocalization |
作者: | 李德暐 Li, De-Wei |
貢獻者: | 劉吉軒 Liu, Jyi-Shane 李德暐 Li,De-Wei |
關鍵詞: | 無人機 區域偵察 行為樹 物件偵測 物件追蹤 電子地圖 地理對位 Drone Area reconnaissance Behavior tree Object detection Object tracking Digital map Georeferencing |
日期: | 2020 | 上傳時間: | 1-Feb-2021 | 摘要: | 隨著無人機的技術越來越成熟，從一開始發展目的為戰爭武器，到現在民眾能使用娛樂用途的空拍機，無人機慢慢地融入了我們的生活之中。無人機擁有獨特空間運動能力，因此出現越來越多的應用。像是美國Airware公司推出無人機屋頂檢查系統，無人機能夠自動收集房屋範圍內的影像資訊等。無人機展現出強大的資訊蒐集能力。雖然目前無人機用於偵察的例子很多，但大都是以高階的機種為主，原因是需要更多的感測器去幫助執行偵察任務，像是熱像儀、鐳射掃描儀等等感測器。然而目前政府機構、民間機構所擁有比較多的機種都是較為便宜的低階微型無人機。有鑑於此，本研究希望能在低成本微型無人機上能使用。基於視覺使用電腦視覺輔助偵測物件並定位，搭配行為樹使其自動控制化減少人力上控制的需求。 As the technology of drones has become more and more mature, from the beginning of the development of weapons of war, to now that people can use aerial cameras for entertainment purposes, drones have slowly integrated into our lives. UAVs have unique space movement capabilities, so more and more applications appear. For example, the United States Airware company launched a drone roof inspection system, which can automatically collect image information within the house. Unmanned aerial vehicles demonstrate powerful information gathering capabilities. Although there are many examples of drones used for\ninvestigation, most of them are based on high-end drones. The reason is that more sensors are needed to help perform investigation tasks, such as thermal imaging cameras, laser scanners, etc. However, at present, government agencies and private organizations have a relatively large number of aircraft types that are relatively inexpensive low-end micro drones. In view of this, this research hopes to be used on low-cost micro drones. Based on vision, computer vision is used to assist in detecting and positioning objects, and the behavior tree is used to make it automatically controlled to reduce the need for human control. |
參考文獻: | [1] R. Austin, Unmanned Aircraft Systems: UAVS Design Development and Deployment, Chichester, U.K.:Wiley, Apr. 2010.\n[2] C. Sandbrook, "The social implications of using drones for biodiversity conservation", Ambio, vol. 44, no. Suppl 4, (S4), pp. 636-647, 2015.\n[3] Parrot Drones SAS (n.d.). Retrieved November 15,2020,from https://support.parrot.com/global/support/products\n[4] C. Bolkcom and E. Bone, "Unmanned aerial vehicles: Background and issues for congress report for congress congressional research service", Proc. Libr. Congr., pp. 13, 2003.\n[5] K. Daniel, S. Rohde, and C. Wietfeld, “Leveraging public wireless communication infrastructures for UAV-based sensor networks,” in Proc. IEEE Int. Conf. Technol. Homeland Secur., Nov. 2010, pp. 179–184.\n[6] S. Nebikera, A. Annena, M. Scherrerb and D. Oeschc, "A lightweight multispectral sensor for micro U A V -Opportunities for very high resolution airborne remote sensing", Int. Archiv. Photogram. Remote Sens. Spatial Inform. Sci, vol. 37, no. B1, pp. 1193-2000, 2008.\n[7] C. Korpela, T. Danko, and P. Oh, "MM-UAV: Mobile manipulating unmanned aerial vehicle," Journal of Intelligent and Robotics Systems, vol. 65, no. 1, pp. 93-101, 2012.\n[8] D. Erdos, A. Erdos and S. E. Watkins, "An experimental UAV system for search and rescue challenge", IEEE Aerospace and Electronic Systems Magazine, vol. 28, no. 5, pp. 32-37, May 2013.\n[9] UAV CHALLENGE Retrieved November 15,2020, from https://uavchallenge.org/about/\n[10] N. Michael, D. Mellinger, Q. Lindsey, and V Kumar, "The grasp multiple micro uav testbed," IEEE Robotics and Automation Magazine, Sept. 2010.\n[11] T. Shima and S. Rasmussen, UAV Cooperative Decision and Control: Challenges and Practical Approaches, SIAM, 2009.\n[12] M. Colledanchise and P. Ögren,"Behavior trees in robotics and AI: An introduction",CoRR,2017,[online]Available:http://arxiv.org/abs/1709.00084.\n[13] P. Rudol and P. Doherty, "Human body detection and geolocalization for UAV search and rescue missions using color and thermal imagery", Proc. IEEE Aerosp. Conf., pp. 1-8, Mar. 2008.\n[14] I. Martinez-Alpiste, G. Golcarenarenji, Q. Wang, and J. Calero, " Altitude-Adaptive and Cost-Effective Object Recognition in an Integrated Smartphone and UAV System." In 2020 European Conference on Networks and Communications (EuCNC) (pp. 316-320). IEEE.\n[15] P. Ogren, "Increasing Modularity of UAV Control Systems using Computer Game Behavior Trees", AIAA Guidance Navigation and Control Conference, 2012.\n[16] K. Y. W. Scheper, S. Tijmons, C. C. de Visser and G. C. H. E. de Croon, "Behaviour trees for evolutionary robotics", Artificial Life, vol. 22, no. 1, pp. 23-48, 2016.\n[17] A. Klöckner, "Behavior trees for uav mission management", INFORMATIK 2013: Informatik angepasst an Mensch Organisation und Umwelt, pp. 57-68, 2013.\n[18] M. Colledanchise and P. Ögren, "How behavior trees modularize robustness and safety in hybrid systems", Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 1482-1488, Jun. 2014.\n[19] H. H. Helgesen, F. S. Leira, T. A. Johansen and T. I. Fossen, Detection and Tracking of Floating Objects Using a UAV with Thermal Camera, Cham:Springer International Publishing, pp. 289-316, 2017.\n[20] S. Drake, "Converting GPS coordinates [phi Lambda h] to navigation coordinates (ENU)", 2002.\n[21] E. M. Hemerly, “Automatic georeferencing of images acquired by\nuav’s,” International Journal of Automation and Computing, vol. 11,\nno. 4, pp. 347–352, Aug 2014.\n[22] H. Xiang and L. Tian, "Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (UAV) platform", Biosystems Engineering, vol. 108, pp. 104-113, 2011.\n[23] L . Deng and D. Yu, "Deep learning: Methods and applications", Foundations and Trends in Signal Processing, vol. 7, no. 3–4, pp. 197-387, 2014.\n[24] Z. Zhao, P. Zheng, S. Xu and X. Wu, "Object detection with deep learning: a review", IEEE Trans. on Neural Net. and Learning Systems, pp. 1-21, January 2019.\n[25] X. Liu, Z. Deng and Y. Yang, "Recent progress in semantic image segmentation", Artificial Intelligence Review, pp. 1-18, 2018.\n[26] R. Girshick, J. Donahue, T. Darrell and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation", Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 580-587, 2014.\n[27] R. Girshick, "Fast R-CNN", Proc. IEEE Int. Conf. Comput. Vis., pp. 1440-1448, 2015.\n[28] W. Liu, D. Anguelov, D. Erhan, C. Szegedy and S. Reed, "SSD: Single shot multibox detector", 2015.\n[29] J. Redmon, S. Divvala, R. Girshick and A. Farhadi, "You only look once: Unified real-time object detection", Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR), pp. 779-788, 2016.\n[30] J. Redmon and A. Farhadi, "YOLO9000: Better faster stronger", Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 6517-6525, 2017.\n[31] J. Redmon and A. Farhadi, "YOLOv3: An incremental improvement" in arXiv:1804.02767, 2018, [online] Available: http://arxiv.org/abs/1804.02767.\n[32] A. Bochkovskiy, C.-Y. Wang and H.-Y. M. Liao, YOLOv4: Optimal Speed and Accuracy of Object Detection, 2020, [online] Available: http://arxiv.org/abs/2004.10934.\n[33] Y. Wu, J. Lim and M. H. Yang, "Online object tracking: A benchmark", Proc. Comput. Vis. Pattern Recognit., pp. 2411-2418, 2013.\n[34] D. Comaniciu, V. Ramesh and P. Meer, "Kernel-based object tracking", IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 5, pp. 564-577, May. 2003.\n[35] A. Yilmaz, X. Li and M. Shah, "Object contour tracking using level sets", Proc. Asian Conf. Computer Vision, 2004.\n[36] X. Farhodov, O. Kwon, K. W. Kang, S. Lee and K. Kwon, "Faster RCNN detection based OpenCV CSRT tracker using drone data", International Conference on Information Science and Communications Technologies (ICISCT), pp. 1-3, 2019.\n[37] L. Tan, X. Dong, Y. Ma and C. Yu, "A multiple object tracking algorithm based on YOLO detection", Int. Congress Image Signal Processing BioMedical Engineering Informatics, pp. 1-5, 2018.\n[38] T. Vincenty, "Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations", Survey Review, vol. 22, no. 176, pp. 88-93, 1975.\n[39] Bebop_autonomy.(n.d.). Retrieved November 15,2020, from https://bebopautonomy.readthedocs.io/en/latest/\n[40] E. Olson, "April`Tag: A robust and flexible visual fiducial system", 2011 IEEE International Conference on Robotics and Automation, May 2011.\n[41] 內政部國土測繪中心Retrieved November 15,2020, from https://maps.nlsc.gov.tw/ | 描述: | 碩士 國立政治大學 資訊科學系 107753039 |
資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0107753039 | 資料類型: | thesis |
| Appears in Collections: | 學位論文 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 303901.pdf | 3.74 MB | Adobe PDF2 | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.