| Issue |
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
|
|
|---|---|---|
| Article Number | 00008 | |
| Number of page(s) | 22 | |
| DOI | https://doi.org/10.1051/bioconf/20249700008 | |
| Published online | 05 April 2024 | |
- Adrian Carrio, Carlos Sampedro, Alejandro Rodriguez-Ramos, and Pascual Campoy, “A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles“, Hindawi Journal of Sensors Volume 2017, Article ID 3296874, 13 pages, (2017). [Google Scholar]
- J. L. Sanchez-Lopez, M. Molina, H. Bavle, Carlos Sampedro, Ramón A. Suárez Fernandez, “A multilayered component-based approach for the development of aerial robotic systems: The aerostack framework,” Journal of Intelligent & Robotic Systems, pp. 1–27, (2017). [Google Scholar]
- Markus Teigen Pike, “ Computer Vision and Deep Learning in Autonomous Drones”, Norwegian University of Science and Technology (NTNU), June (2017). [Google Scholar]
- Tianpei Liao, Amal Haridevan, Yibo Liu, Jinjun Shan, “Autonomous Vision-based UAV Landing with Collision Avoidance using Deep Learning”, arXiv: 2109.08628v1[cs.LG], 17 Sep (2021). [Google Scholar]
- Lucas Prado Osco, Jose Marcato Junior, Ana Paula Marques Ramos, Lúcio Andre de Castro Jorge, Sarah Narges Fatholahi, Jonathan de Andrade Silva, Edson Takashi Matsubara, Hemerson Pistori, Wesley Nunes Gonçalves, Jonathan Li, “A review on deep learning in UAV remote sensing”, International Journal of Applied Earth Observations and Geoinformation 102 (2021) 102456, 27 July (2021). [Google Scholar]
- Xin Liu and Zhanyue Zhang, “A Vision-Based Target Detection, Tracking, and Positioning Algorithm for Unmanned Aerial Vehicle”, Wireless Communications and Mobile Computing Volume 2021, Article ID 5565589, 12 pages, (2021). [Google Scholar]
- Farhad Samadzadegan, Farzaneh Dadrass Javan, Farnaz Ashtari Mahini and Mehrnaz Gholamshahi, “Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery”, journal of aerospace, 9, 31, 10 January, (2022). [Google Scholar]
- Li-Hua Wen and Kang-Hyun Jo, “Deep learning-based perception systems for autonomous driving: A comprehensi survey”, Neurocomputing 489 (2022) 255–270, 17 March (2022). [CrossRef] [Google Scholar]
- Ahmed Abdulhameed and Qurban A. Memon, “Support Vector Machine Based Design and Simulation of Air Traffic Management for Prioritized Landing of Large Number of UAVs”, European Journal of Artificial Intelligent and Machine Learning, Vol 1|Issue 2|April, (2022). [Google Scholar]
- Subrahmanyam Vaddi, Dongyoun Kim, Chandan Kumar, Shafqat Shad & Ali Jannesari, “Efficient object detection model for real-time UAV applications” Computer and Information Science; Vol. 14, No. 1; 2021 ISSN 1913-8989 E-ISSN 1913-8997 Published by Canadian Center of Science and Education Online Published: January 22, 2021, doi:10.5539/cis.v14n1p45. [Google Scholar]
- Ren-Yi Kung, et al., “Application of Deep Learning and Unmanned Aerial Vehicle Building Maintenance”, Advances in Civil Engineering Volume 2021, Article ID 5598690, 12 pages https://doi.org/10.1155/2021/5598690. [Google Scholar]
- Tanmay Kumar Behera, Sambit Bakshi, Pankaj Kumar Sa, “Vegetation Extraction from UAV-based Aerial Images through Deep Learning” Computers and Electronics in Agriculture, https://doi.org/10.1016/j.compag.2022.107094, Volume 198, July (2022). [Google Scholar]
- Maissa Hamouda, Karim Saheb Ettabaa, Med Salim Bouhlel, “Smart feature extraction and classification of hyperspectral images based on convolutional neural networks” IET Image Processing the Institution of Engineering and Technology 2020, Vol. 14 Iss. 10, pp. 1999–2005, (2020). [Google Scholar]
- Ruben Geraldes et al, “UAV-Based Situational Awareness System Using Deep Learning”, IEEE access Digital Object Identifier 10.1109/ACCESS.2019.2938249, volume 7, 29, (2019). [Google Scholar]
- Evangelos Maltezos et al, The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets, www.mdpi.com/journal/computation, Computation 2021, 9, 12. https://doi.org/10.3390/computation9020012(2021). [Google Scholar]
- Ahmed Nassar, Karim Amer, Reda ElHakim, Mohamed ElHelw “A Deep CNN-Based Framework for Enhanced Aerial Imagery Registration with Applications to UAV Geolocalization”, IEEE Xplore, CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)18–22 June 2018, (2018). [Google Scholar]
- David Safadinho, João Ramos, Roberto Ribeiro, Vítor Filipe, João Barroso and António Pereira, “UAV Landing Using Computer Vision Techniques for Human Detection” journal/sensors (MDPI), 20, 613, (2020). [Google Scholar]
- Efstratios Kakaletsis, Charalampos Symeonidis, Maria Tzelepi, Ioannis Mademlis, Anastasios Tefas, Nikos Nikolaidis, and Ioannis Pitas “Computer Vision for Autonomous UAV Flight Safety: An Overview and a Vision-based Safe Landing Pipeline Example”, Vol. 1, No. 1, Article. Publication date:(2020). [Google Scholar]
- Jatin Upadhyay, Abhishek Rawat and Dipankar Deb *, “multiple Drone Navigation and Formation Using Selective Target Tracking-Based Computer Vision”, journal/electronics (MDPI), 10, 2125, (2021). [Google Scholar]
- Phong Ha Nguyen, Muhammad Arsalan, Ja Hyung Koo1, Rizwan Ali Naqvi, Noi Quang Truong and Kang Ryoung Park “LightDenseYOLO: A Fast and Accurate Marker Tracker for Autonomous UAV Landing by Visible Light Camera Sensor on Drone”, Journal of Applied Sciences(MDPI), 18, 1703, (2018). [Google Scholar]
- Mateusz Apońl, Arkadiusz Nikonowiczl, Leszek Ambroziakl, Mirosław Kondratiukl, Piotr Burzyński and Adam Kuczyński, “Vision-Based Autonomous Object Tracking for Unmanned Aerial Vehicles”, AIP Conference Proceedings, 29 October (2018). [Google Scholar]
- Yiming Zhao, “Empowering Computer Vision Models with Deep Learning for Robotic Perception and Drone Geolocation”, Worcester Polytechnic Institute Phd Electrical and Computer Engineering, (2021). [Google Scholar]
- Jihad Kadhim Abd Ali and Maysoon Khazaal Abbas Maaroof, “Phase Change Analysis of Robot Laser Drilling with Accuracy Enhancement by Deep Learning”, International Journal of Analytical, Experimental and Finite Element Analysis Volume 9, Issue 3, pp 50–61, Sept (2022). [Google Scholar]
- Seokwon Yeom and In-Jun Cho, “Detection and Tracking of Moving Pedestrians with aSmall Unmanned Aerial Vehicle”, Journal of Applied Sciences(MDPI) 9, 3359, (2019). [CrossRef] [Google Scholar]
- Jihad Kadhim Abd Ali and Maysoon Khazaal Abbas Maaroof, “Theoretical Investigations of Wing Coating on the Performance of UAV”, International Journal of Advances in Engineering and Emerging Technology (IJAEET) Vol. 13, No. 2, pp 88–100, September (2022). [Google Scholar]
- Adriano Garcia, Sandeep S. Mittal, Edward Kiewra and Kanad Ghose, “A Convolutional Neural Network Vision System Approach to Indoor Autonomous Quadrotor Navigation”, The 23rd Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction - PRES’20, (2019). [Google Scholar]
- Florian Olsner, Stefan Milz,“Catch Me, If You Can! A Mediated Perception Approach Towards Fully Autonomous Drone Racing” Proceedings of Machine Learning Research 123:90–99, (2020). [Google Scholar]
- M. Saifizi, N. Syahirah, Wan Azani Mustafa, Hasliza A Rahim, Mohd Wafi Nasrudin, “Using Unmanned Aerial Vehicle in 3D Modelling of UniCITI Campus to Estimate Building Size”, The 1st International Conference on Engineering and Technology (ICoEngTech) 2021, Journal of Physics: Conference Series, (2021). [Google Scholar]
- S. Mantey and M. S. Aduah, “Comparative Analysis of Stockpile Volume Estimation using UAV and GPS Techniques”, GMJ Vol. 21, No. 1, June., (2021). [Google Scholar]
- Tianqi Wang and Dong Eui Chang, “Robust Navigation for Racing Drones based on Imitation Learning and Modularization”, arXiv:2105.12923v1[cs.RO], 27 May (2021). [Google Scholar]
- Li-Yu Lo, Chi Hao Yiu, Yu Tang, An-Shik Yang, Boyang Li and Chih-Yung Wen, “Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications”, journal/sensors, 21, 7888, 27 November (2021). [Google Scholar]
- Yunlong Song, Mats Steinweg, Elia Kaufmann, and Davide Scaramuzza, “Autonomous Drone Racing with Deep Reinforcement Learning” International Conference on Intelligent Robots and Systems (IROS), Prague, 2021. ©IEEE, (2021). [Google Scholar]
- Leonard Bauersfeld, Elia Kaufmann, Philipp Foehn, Sihao Sun, Davide Scaramuzza, “NeuroBEM: Hybrid Aerodynamic Quadrotor Model”, Science and Systems 2021 conference, (2021). [Google Scholar]
- Antonio Loquercio, Alessandro Saviolo, and Davide Scaramuzza, “AutoTune: Controller Tuning for High-Speed FlightScaramuzza”, arXiv:2103.10698v2 [cs.RO] 8 Feb (2022). [Google Scholar]
- J. A. García-Pulido, G. Pajares, S. Dormido, UAV Landing Platform Recognition Using Cognitive Computation Combining Geometric Analysis and Computer Vision Techniques”, published online8 June, (2022). [Google Scholar]
- Christian Pfeiffer, Simon Wengeler, Antonio Loquercio, Davide Scaramuzza, “Visual Attention Prediction Improves Performance of Autonomous Drone Racing Agents”, journal. pone.0264471, Research Article: PLOS ONE Vol. 17, Issue 3, (2022). [Google Scholar]
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