A generalized higher order neural network for aircraft recognition in a video docking system

Ali, Syed Zafar; Choudhry, M.A.

doi:10.1007/s00521-008-0224-0

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…Saghafi et al [30] evaluate multilayer neural networks for classifying aircraft via simulated training data using photos from 3D aircraft models (helicopters and propellers). Likewise, Ali and Choudhry [31] classify civil airliners using feedforward neural networks for video docking systems. Their developed models can classify a small range of the aircraft type/models, while operations at general aviation airports include a wide range of aircraft from propellers to jet airliners.…”

Section: A Vision-based Methods 1) Aircraft Classificationmentioning

confidence: 99%

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

2022

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Aircraft identification in airport operations is critical to various applications, including airport planning and environmental studies. Previous research and commercially available systems heavily rely on recognizing aircraft tail numbers using text recognition. However, this approach alone does not provide accurate results in situations when the tail number visibility is reduced or obstructed. Furthermore, general aviation aircraft are harder to identify because they are small in size, and their tail numbers include substantial variations in fonts, sizes, and orientations. To tackle these issues, we propose a two-step computer vision-based aircraft identification method, first identifying the aircraft type and then recognizing the tail number in a probabilistic multi-frame-based (MFB) framework. In the first step, a convolutional neural network (CNN)-based aircraft classifier is customized to decrease the search space in the registration database. In the second step, the identification process is finalized by integrating the text recognition results into the designed probabilistic MFB framework. The proposed method achieves approximately 90% identification accuracy when tested on video data collected from three general aviation airports. This is a significant improvement compared to text recognition alone, which recognizes 67% of the individual tail number characters.INDEX TERMS Intelligent transportation systems, computer vision, airports, aircraft.

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Section: A Vision-based Methods 1) Aircraft Classificationmentioning

confidence: 99%

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

2022

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“…Using a multi-layer neural network and photos from a 3D aircraft model database, over 90% recognition rate in real conditions was achieved. Similarly, Ali and Choudhry [3] evaluated neural network architectures for detecting and classifying different aircrafts using a video docking system. Besada et al [2] is probably the most similar work to ours and its results are also presented in Besada et al [5] and Berlanga et al [4].…”

Section: Related Workmentioning

confidence: 99%

“…Since the location of the registration number is more or less known in advance, it would be feasible to identify it using a low cost, computer vision system. Application of vision systems to air traffic management for detection and identification of aircraft in the vicinity of an airport has been an area of research for some years [2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Facilitation of air traffic control via optical character recognition‐based aircraft registration number extraction

Vidakis

Kosmopoulos²

2018

IET Intelligent Transport Systems

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“…From general information, these systems can perform specific tasks such as aircraft docking guidance [60] or turnaround activities identification [57].…”

Section: Specific Computer Vision Functionsmentioning

confidence: 99%

A computer vision framework to detect, track and identify objects in a wide field-of-view airport-airside environment

Thai¹

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To accommodate the steady growth of air traffic, airports worldwide need to increase their capacity by expanding infrastructure and optimizing air traffic procedures. However, as airports add new infrastructure (runways, taxiways, and aprons) into operation, they also increase the challenge of airport airside management. This may lead to an increase in controller workload as well as runway and taxiway incursion events. This problem is compounded by unauthorized access to the control zone by unauthorized and non-cooperative objects, such as drones. Air traffic controllers now are therefore required to monitor more traffic and direct complex operations. For effective airport airside management, controllers must detect aircraft movement on the ground. They also must coordinate push-back procedures, taxiway routing, and runway sequencing for effective airside traffic circulation. Controllers are traditionally located in a tall control tower to manage air traffic through an out-of-window view. Although physical towers have served airside control well, they have several limitations. First, a physical tower is costly for a small airport because the fixed costs for providing air traffic service are independent of the traffic. Secondly, one physical tower might not be sufficient to visually cover a large airport. For full visual traffic coverage of a large airport, the geometric height of a tower can be sustainably high which poses an obstruction to navigation in the airport control zone.To enhance safety and improve operational efficiency, the concept of the digital tower has been developed. A digital tower replaces the out-of-window view of a conventional tower ix with a visualization system provided by a network of high-resolution cameras. By using such a visualization system, an airport can have more than one digital tower if necessary, resulting in greater visual coverage. The visibility can be further enhanced by infrared and pan-tiltzoom cameras. With a digital system, digital towers can provide many enhanced functions to assist controllers. However, the airport airside is a wide field-of-view environment with dynamic traffic and complex operations, and existing computer vision systems at airports only focus on a portion of the airport airside, such as detecting an aircraft on the runway, tracking moving aircraft, or detecting objects on an apron. Moreover, such systems do not consider gate site management, such as push-back control or turnaround monitoring.The objective of this thesis is to develop a computer vision framework to detect, track and identify aircraft and relevant objects (cooperative and non-cooperative) in a wide field-of-view (multiple cameras) airside for effective airside monitoring and turnaround management. The framework designs a specific Convolutional Neural Network, namely AirNet, customized for the unique characteristics of the airport airside environment. By using depthwise convolution operation, the computational costs are significantly reduced while maintaining high detection performance...

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A generalized higher order neural network for aircraft recognition in a video docking system

Cited by 10 publications

References 8 publications

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

General Aviation Aircraft Identification at Non-Towered Airports Using a Two-Step Computer Vision-Based Approach

Facilitation of air traffic control via optical character recognition‐based aircraft registration number extraction

A computer vision framework to detect, track and identify objects in a wide field-of-view airport-airside environment

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