Relevant bibliographies by topics / Vision-Based Aircraft Detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Conference papers

Academic literature on the topic 'Vision-Based Aircraft Detection'

Author: Grafiati
Published: 11 December 2022
Last updated: 26 January 2023

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Journal articles on the topic "Vision-Based Aircraft Detection"

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Holak, Krzysztof, and Wojciech Obrocki. "Vision-based damage detection of aircraft engine’s compressor blades." Diagnostyka 22, no. 3 (August 31, 2021): 83–90. http://dx.doi.org/10.29354/diag/141589.

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Ramalingam, Balakrishnan, Vega-Heredia Manuel, Mohan Rajesh Elara, Ayyalusami Vengadesh, Anirudh Krishna Lakshmanan, Muhammad Ilyas, and Tan Jun Yuan James. "Visual Inspection of the Aircraft Surface Using a Teleoperated Reconfigurable Climbing Robot and Enhanced Deep Learning Technique." International Journal of Aerospace Engineering 2019 (September 12, 2019): 1–14. http://dx.doi.org/10.1155/2019/5137139.

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Aircraft surface inspection includes detecting surface defects caused by corrosion and cracks and stains from the oil spill, grease, dirt sediments, etc. In the conventional aircraft surface inspection process, human visual inspection is performed which is time-consuming and inefficient whereas robots with onboard vision systems can inspect the aircraft skin safely, quickly, and accurately. This work proposes an aircraft surface defect and stain detection model using a reconfigurable climbing robot and an enhanced deep learning algorithm. A reconfigurable, teleoperated robot, named as “Kiropter,” is designed to capture the aircraft surface images with an onboard RGB camera. An enhanced SSD MobileNet framework is proposed for stain and defect detection from these images. A Self-filtering-based periodic pattern detection filter has been included in the SSD MobileNet deep learning framework to achieve the enhanced detection of the stains and defects on the aircraft skin images. The model has been tested with real aircraft surface images acquired from a Boeing 737 and a compact aircraft’s surface using the teleoperated robot. The experimental results prove that the enhanced SSD MobileNet framework achieves improved detection accuracy of aircraft surface defects and stains as compared to the conventional models.
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James, Jasmin, Jason J. Ford, and Timothy L. Molloy. "Quickest Detection of Intermittent Signals With Application to Vision-Based Aircraft Detection." IEEE Transactions on Control Systems Technology 27, no. 6 (November 2019): 2703–10. http://dx.doi.org/10.1109/tcst.2018.2872468.

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Zhou, Liming, Haoxin Yan, Yingzi Shan, Chang Zheng, Yang Liu, Xianyu Zuo, and Baojun Qiao. "Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks." Journal of Electrical and Computer Engineering 2021 (August 11, 2021): 1–16. http://dx.doi.org/10.1155/2021/4685644.

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Aircraft detection for remote sensing images, as one of the fields of computer vision, is one of the significant tasks of image processing based on deep learning. Recently, many high-performance algorithms for aircraft detection have been developed and applied in different scenarios. However, the proposed algorithms still have a series of problems; for instance, the algorithms will miss some small-scale aircrafts when applied to the remote sensing image. There are two main reasons for the problem; one reason is that the aircrafts in the remote sensing image are usually small in size, leading to detecting difficulty. The other reason is that the background of the remote sensing image is usually complex, so the algorithms applied to the scenario are easy to be affected by the background. To address the problem of small size, this paper proposes the Multiscale Detection Network (MSDN) which introduces a multiscale detection architecture to detect small-scale aircrafts. With the intention to resist the background noise, this paper proposes the Deeper and Wider Module (DAWM) which increases the perceptual field of the network to alleviate the affection. Besides, to address the two problems simultaneously, this paper introduces the DAWM into the MSDN and names the novel network structure as Multiscale Refined Detection Network (MSRDN). The experimental results show that the MSRDN method has detected the small-scale aircrafts that other algorithms missed and the performance indicators have higher performance than other algorithms.
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Molloy, Timothy L., Jason J. Ford, and Luis Mejias. "Detection of aircraft below the horizon for vision-based detect and avoid in unmanned aircraft systems." Journal of Field Robotics 34, no. 7 (May 16, 2017): 1378–91. http://dx.doi.org/10.1002/rob.21719.

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Wang, Zhenyu, Yanyun Wang, Wei Cheng, Tao Chen, and Hui Zhou. "A monocular vision system based on cooperative targets detection for aircraft pose measurement." Journal of Physics: Conference Series 887 (August 2017): 012029. http://dx.doi.org/10.1088/1742-6596/887/1/012029.

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Minwalla, Cyrus, Dan Tulpan, Nabil Belacel, Fazel Famili, and Kristopher Ellis. "Detection of Airborne Collision-Course Targets for Sense and Avoid on Unmanned Aircraft Systems Using Machine Vision Techniques." Unmanned Systems 04, no. 04 (October 2016): 255–72. http://dx.doi.org/10.1142/s2301385016500102.

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Detecting collision-course targets in aerial scenes from purely passive optical images is challenging for a vision-based sense-and-avoid (SAA) system. Proposed herein is a processing pipeline for detecting and evaluating collision course targets from airborne imagery using machine vision techniques. The evaluation of eight feature detectors and three spatio-temporal visual cues is presented. Performance metrics for comparing feature detectors include the percentage of detected targets (PDT), percentage of false positives (POT) and the range at earliest detection ([Formula: see text]). Contrast and motion-based visual cues are evaluated against standard models and expected spatio-temporal behavior. The analysis is conducted on a multi-year database of captured imagery from actual airborne collision course flights flown at the National Research Council of Canada. Datasets from two different intruder aircraft, a Bell 206 rotor-craft and a Harvard Mark IV trainer fixed-wing aircraft, were compared for accuracy and robustness. Results indicate that the features from accelerated segment test (FAST) feature detector shows the most promise as it maximizes the range at earliest detection and minimizes false positives. Temporal trends from visual cues analyzed on the same datasets are indicative of collision-course behavior. Robustness of the cues was established across collision geometry, intruder aircraft types, illumination conditions, seasonal environmental variations and scene clutter.
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Campa, G., M. R. Napolitano, M. Perhinschi, M. L. Fravolini, L. Pollini, and M. Mammarella. "Addressing pose estimation issues for machine vision based UAV autonomous serial refuelling." Aeronautical Journal 111, no. 1120 (June 2007): 389–96. http://dx.doi.org/10.1017/s0001924000004644.

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Abstract This paper describes the results of an effort on the analysis of the performance of specific ‘pose estimation’ algorithms within a Machine Vision-based approach for the problem of aerial refuelling for unmanned aerial vehicles. The approach assumes the availability of a camera on the unmanned aircraft for acquiring images of the refuelling tanker; also, it assumes that a number of active or passive light sources – the ‘markers’ – are installed at specific known locations on the tanker. A sequence of machine vision algorithms on the on-board computer of the unmanned aircraft is tasked with the processing of the images of the tanker. Specifically, detection and labeling algorithms are used to detect and identify the markers and a ‘pose estimation’ algorithm is used to estimate the relative position and orientation between the two aircraft. Detailed closed-loop simulation studies have been performed to compare the performance of two ‘pose estimation’ algorithms within a simulation environment that was specifically developed for the study of aerial refuelling problems. Special emphasis is placed on the analysis of the required computational effort as well as on the accuracy and the error propagation characteristics of the two methods. The general trade offs involved in the selection of the pose estimation algorithm are discussed. Finally, simulation results are presented and analysed.
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Chahl, Javaan, and Aakash Dawadee. "Towards an Optical Aircraft Navigation System." Applied Mechanics and Materials 629 (October 2014): 321–26. http://dx.doi.org/10.4028/www.scientific.net/amm.629.321.

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Navigation by means that are fully self contained, without the weight and cost of high performance inertial navigation units is highly desirable in many applications both military and civilian. In this paper we introduce a suite of sensors and behaviors that include: the means to reduce lateral drift due to wind using optical flow, detection of a constellation of landmarks using a machine vision system, and a polarization compass that is reliable at extreme latitudes based on polarization. In a series of flight trials and detailed simulations we have demonstrated that a combination of these functions achieves purely optical navigation with simplicity and robustness.
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Foo, Simon Y. "A rule-based machine vision system for fire detection in aircraft dry bays and engine compartments." Knowledge-Based Systems 9, no. 8 (December 1996): 531–40. http://dx.doi.org/10.1016/s0950-7051(96)00005-6.

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Dissertations / Theses on the topic "Vision-Based Aircraft Detection"

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Shah, Syed Irtiza Ali. "Vision based 3D obstacle detection." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29741.

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Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2010.
Committee Co-Chair: Johnson, Eric; Committee Co-Chair: Lipkin, Harvey; Committee Member: Sadegh, Nader. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Vendra, Soujanya. "Addressing corner detection issues for machine vision based UAV aerial refueling." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4551.

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Thesis (M.S.)--West Virginia University, 2006.
Title from document title page. Document formatted into pages; contains xi, 121 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 90-95).
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James, Jasmin. "Quickly detecting aircraft in image sequences." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/133291/1/Jasmin%20James%20Thesis-locked.pdf.

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This thesis explores the problem of detecting an aircraft on a mid-air collision course encounter with the goal of contributing to the development of vision-based SAA systems for use in the national airspace. Contributions are made in the vision-based aircraft detection application as well as in advancing quickest change detection theory.
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Alsalam, Bilal. "A small autonomous UAV for detection and action in precision agriculture." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/104318/1/Bilal%20Hazim%20Younus_Alsalam_Thesis.pdf.

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This thesis develops a framework for Unmanned Aerial Vehicles (UAVs) with on-board computer for the purpose of detection and action in agriculture and other Remote Sensing tasks. This system has potential applications in the field of precision agriculture such as, invasive weed detection and eradication. The method is based on vision-based-detection and navigation that autonomously detects a target (e.g. weed) and takes action, such as spraying herbicide. The system was tested in simulation and in outdoors experiments at a farm in south-east Queensland, Australia. The results of this system have shown that the on-board system is capable of detecting targets of interest and taking autonomous actions accurately and efficiently which makes it’s a good addition to precision agriculture.
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Books on the topic "Vision-Based Aircraft Detection"

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Octavia, Camps, and United States. National Aeronautics and Space Administration., eds. Detection of obstacles on runway using Ego-Motion compensation and tracking of significant features: An interim technical report for NASA Ames cooperation agreement no. NCC2-916 "a vision-based obstacle detection system for aircraft navigation" period of the grant, August 1, 1995 to July 31, 1997. [Washington, DC: National Aeronautics and Space Administration, 1996.

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Octavia, Camps, and United States. National Aeronautics and Space Administration., eds. Detection of obstacles in monocular image sequences: Final technical report for NASA co-operative research agreement number NCC 2-916, "A vision-based obstacle detection system for aircraft navigation," period of grant--August 1, 1995 to July 31, 1997. [Washington, DC: National Aeronautics and Space Administration, 1997.

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Detection of obstacles in monocular image sequences: Final technical report for NASA co-operative research agreement number NCC 2-916, "A vision-based obstacle detection system for aircraft navigation," period of grant--August 1, 1995 to July 31, 1997. [Washington, DC: National Aeronautics and Space Administration, 1997.

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Conference papers on the topic "Vision-Based Aircraft Detection"

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Leong, Wai Lun, Pengfei Wang, Sunan Huang, Zhengtian Ma, Hong Yang, Jingxuan Sun, Yu Zhou, Mohamed Redhwan Abdul Hamid, Sutthiphong Srigrarom, and Rodney Teo. "Vision-Based Sense and Avoid with Monocular Vision and Real-Time Object Detection for UAVs." In 2021 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2021. http://dx.doi.org/10.1109/icuas51884.2021.9476746.

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Molloy, Timothy L., Jason J. Ford, and Luis Mejias. "Adaptive detection threshold selection for vision-based sense and avoid." In 2017 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2017. http://dx.doi.org/10.1109/icuas.2017.7991313.

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Zhang, Zhouyu, Yunfeng Cao, Meng Ding, Likui Zhuang, and Weiwen Yao. "An intruder detection algorithm for vision based sense and avoid system." In 2016 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2016. http://dx.doi.org/10.1109/icuas.2016.7502521.

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Yuan, Chi, Zhixiang Liu, and Youmin Zhang. "Vision-based forest fire detection in aerial images for firefighting using UAVs." In 2016 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2016. http://dx.doi.org/10.1109/icuas.2016.7502546.

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Yifei, Zhang, Feng Can, Liu Yayan, Li Ruijie, and Lei Aiqiang. "Study on aircraft speed measurement method based on computer vision of high-speed camera." In Sixth Symposium on Novel Photoelectronic Detection Technology and Application, edited by Huilin Jiang and Junhao Chu. SPIE, 2020. http://dx.doi.org/10.1117/12.2563860.

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Abu-Jbara, Khaled, Wael Alheadary, Ganesh Sundaramorthi, and Christian Claudel. "A robust vision-based runway detection and tracking algorithm for automatic UAV landing." In 2015 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2015. http://dx.doi.org/10.1109/icuas.2015.7152407.

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Briese, Christoph, Andreas Seel, and Franz Andert. "Vision-based detection of non-cooperative UAVs using frame differencing and temporal filter." In 2018 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2018. http://dx.doi.org/10.1109/icuas.2018.8453372.

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James, Jasmin, Jason J. Ford, and Timothy L. Molloy. "Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid." In 2019 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2019. http://dx.doi.org/10.1109/icuas.2019.8798096.

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James, Jasmin, Jason J. Ford, and Timothy L. Molloy. "A Novel Technique for Rejecting Non-Aircraft Artefacts in Above Horizon Vision-Based Aircraft Detection." In 2020 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2020. http://dx.doi.org/10.1109/icuas48674.2020.9213938.

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Molloy, Timothy L., and Jason J. Ford. "HMM relative entropy rate concepts for vision-based aircraft manoeuvre detection." In 2013 3rd Australian Control Conference (AUCC). IEEE, 2013. http://dx.doi.org/10.1109/aucc.2013.6697240.

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