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AI-Based Rotation Aware Detection of Aircraft and Identification of Key Features for Collision Avoidance Systems (SAE Paper 2022-01-0036)
Technical Paper
2022-01-0036
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Event:
AeroTech
Language:
English
Abstract
Object detection using deep learning is a well-studied area and different neural network architectures have been proposed for localization of objects at an eye-level view. However, detection of airplanes is more challenging as they are not necessarily aligned horizontally or vertically in the input images as is the case in vehicle or people detection. For aircraft detection, horizontal axis-aligned bounding boxes are not precise enough and may contain a plethora of background data. Thus, our approach for aircraft detection proposes to infer additional information about the orientation of the airplane directly from the object detection model. Additionally, we also apply a computer-vision post processing pipeline to find out the specific aircraft features such as tail, head, wings, etc. Combining the obtained angle and additional key features of the airplane allows for determining the direction of travel for aircraft which can be potentially used as a part or as an enhancement of more complex collision avoidance systems. Specifically, this study focuses on an in-depth evaluation of various deep learning-based solutions for the fully automated detection of the aircraft heading direction from multi-instance satellite imagery characterized by rich background and small spatial resolution of objects. The proposed approach was verified on open-source airplane datasets, proving its robustness, high accuracy, and its capability to generalize well to new image sets. Additionally, the presented technique has a potential for automated enhancement of existing datasets with additional information about object orientation or key points, eliminating the need for pixel-wise labelling which is beneficial for various future studies in the aerospace field.
Authors
- Alicja Kwasniewska - SiMa.ai
- Onkar Chougule - SiMa.ai
- Sneha Kondur - SiMa.ai
- Sairam Alavuru - SiMa.ai
- Rey Nicolas - General Atomics Aeronautical Systems Inc.
- David Gamba - SiMa.ai
- Harsha Gupta - SiMa.ai
- Dennis Chen - General Atomics Aeronautical Systems Inc.
- Anastacia MacAllister - General Atomics Aeronautical Systems Inc.
Citation
Kwasniewska, A., Chougule, O., Kondur, S., Alavuru, S. et al., "AI-Based Rotation Aware Detection of Aircraft and Identification of Key Features for Collision Avoidance Systems (SAE Paper 2022-01-0036)," SAE Technical Paper 2022-01-0036, 2022, https://doi.org/10.4271/2022-01-0036.Also In
References
- Berry , K. , Sawyer , M. , Hinson , J. , and Dewalt , L. Quantifying the Human Element of Safety in Airport Tower Operations: Overcoming Risks Introduced by Static Airport Characteristics Procedia Manufacturing 3 2015 2960 2966
- Helzer , S.G. Establishment and Discontinuance Criteria for Airport Traffic Control Towers 1983
- National Business Aviation Association https://nbaa.org/wp-content/uploads/aircraft-operations/safety/operating-into-a-non-towered-airport.pdf st 2021
- U.S. Department of Transportation Federal Aviation Administration https://www.faa.gov/documentLibrary/media/Advisory_Circular/AC_90-66B.pdf th
- Shakhatreh , H. , Sawalmeh , A.H. , Al-Fuqaha , A. , Dou , Z. et al. Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges IEEE Access 7 2019 48572 48634
- Zeitlin , A.D. and McLaughlin , M.P. Safety of Cooperative Collision Avoidance for Unmanned Aircraft 2006 IEEE/AIAA 25TH Digital Avionics Systems Conference 2006 1 7
- Feng , S. , Sebastian , B. , and Ben-Tzvi , P. A Collision Avoidance Method Based on Deep Reinforcement Learning Robotics 10 2 2021 73
- Wang , C. , Zhang , X. , Cong , L. , Li , J. et al. Research on Intelligent Collision Avoidance Decision-Making of Unmanned Ship in Unknown Environments Evolving Systems 10 4 2019 649 658
- https://www.kaggle.com/airbusgeo/airbus-aircrafts-sample-dataset rd 2021
- Frye , E.O. and Killham , D.E. Aircraft Collision Avoidance Systems IEEE Spectrum 3 1 1966 72 80
- Gazit , R.Y. and Powell , J.D. Aircraft Collision Avoidance based on GPS Position Broadcasts 15th DASC. AIAA/IEEE Digital Avionics Systems Conference 1996 393 399
- Kochenderfer , M.J. , Chryssanthacopoulos , J.P. , Kaelbling , L.P. , and Lozano-Pérez , T. 2010
- Billingsley , T. , Kochenderfer , M. , and Chryssanthacopoulos , J. Collision Avoidance for General Aviation 2011 IEEE/AIAA 30th Digital Avionics Systems Conference 2011 1 17
- Julian , K.D. and Kochenderfer , M.J. Guaranteeing Safety for Neural Network-Based Aircraft Collision Avoidance Systems 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC) 2019 1 10
- Irfan , A. , Julian , K.D. , Wu , H. , Barrett , C. et al. Towards Verification of Neural Networks for Small Unmanned Aircraft Collision Avoidance 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC) 2020 1 10
- Luongo , S. , Di Vito , V. , Fasano , G. , Accardo , D. et al. Automatic Collision Avoidance System: Design, Development and Flight Tests 2011 IEEE/AIAA 30th Digital Avionics Systems Conference 2011 5C1 1
- Mcfadyen , A. , Mejias , L. , Corke , P. , and Pradalier , C. Aircraft Collision Avoidance using Spherical Visual Predictive Control and Single Point Features 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems 2013 50 56
- Mcfadyen , A. , Durand-Petiteville , A. , and Mejias , L. Decision Strategies for Automated Visual Collision Avoidance 2014 International Conference on Unmanned Aircraft Systems (ICUAS) , 2014 715 725
- Rasheed , A. , Alam , A. , Melloni , J. , and Domingo , R. https://asharalam11.github.io/pdf/CS238_Final_Paper.pdf rd 2022
- Krizhevsky , A. , Sutskever , I. , and Hinton , G.E. Imagenet Classification with Deep Convolutional Neural Networks Advances in Neural Information Processing Systems 25 2012 1097 1105
- Zhao , Y. , Wu , R. , Polk , A. , Xi , M. et al. http://rbr.cs.umass.edu/lta/papers/FSS-18_paper_56.pdf rd 2022
- Demir , I. , Koperski , K. , Lindenbaum , D. , Pang , G. et al. Deepglobe 2018: A Challenge to Parse the Earth Through Satellite Images Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2018 172 181
- Tahir , A. , Adil , M. , and Ali , A. arXiv preprint arXiv:2104.11677 2021
- Hassan , A. , Hussein , W.M. , Said , E. , and Hanafy , M.E. A Deep Learning Framework for Automatic Airplane Detection in Remote Sensing Satellite Images 2019 IEEE Aerospace Conference 2019 1 10
- Ucar , F. , Dandil , B. , and Ata , F. Aircraft Detection System based on Regions with Convolutional Neural Networks International Journal of Intelligent Systems and Applications in Engineering 8 3 2020 147 153
- Lin , Y.-C. and Chen , W.-D. Automatic Aircraft Detection in Very-High-Resolution Satellite Imagery using a YOLOv3-Based Process Journal of Applied Remote Sensing 15 1 2021 018502
- Wu , H. , Zhang , H. , Zhang , J. , and Fanjiang X. Fast Aircraft Detection in Satellite Images Based on Convolutional Neural Networks 2015 IEEE International Conference on Image Processing (ICIP) 2015 4210 4214
- Naresh , Y.G. , Little , S. , and O'Connor , N.E. A Residual Encoder-Decoder Network for Semantic Segmentation in Autonomous Driving Scenarios 2018 26th European Signal Processing Conference (EUSIPCO) 2018 1052 1056
- Wu , Y. , Kirillov , A. , Massa , F. , Lo , W.-Y. et al. https://github.com/facebookresearch/detectron2 th 2021
- https://aireverie.com/rareplanes th
- Redmon , J. , Divvala , S. , Girshick , R. , and Farhadi , A. You Only Look Once: Unified, Real-Time Object Detection Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016 779 788
- https://github.com/ultralytics/yolov5 th 2021
- Ge , Z. , Liu , S. , Wang , F. , Li , Z. et al. 2021
- Durand , N. et al. 2021
- Xie , H. , Zhang , M. , Ge , J. , Dong , X. et al. Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation Complexity 2021 2021
- Melián , J.M. , Jiménez , A. , Díaz , M. , Morales , A. et al. Real-Time Hyperspectral Data Transmission for UAV-Based Acquisition Platforms Remote Sensing 13 5 2021 850
- https://github.com/wkentaro/labelme
- https://developer.nvidia.com/blog/detecting-rotated-objects-using-the-odtk/ nd
- https://github.com/NVIDIA/retinanet-examples st
- https://www.flightsimulator.com/ rd