Implementing Haar Cascade Classifiers for Automated Rapid Detection of Light Aircraft at Local Airports

Farhadmanesh, Mohammad; Rashidi, Abbas; Marković, Nikola

doi:10.1061/9780784483893.003

Cited by 4 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The image-based approach for measuring the aircraft operations presented in [22], [23] focuses on counting the operations. Another research line is the image classification methods to find the aircraft type or model.…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Two major elements of the GA airport operational data are aircraft identities and operations count and classification. Concerning aircraft identification, we previously presented a tail number region detector for smaller (light) aircraft in Farhadmanesh et al ( 42 ). Also, to address the identification issues in extreme lighting conditions for GA aircraft, Farhadmanesh et al developed a two-step vision-based method that first recognizes the aircraft type and then recognizes its tail number ( 43 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Neural network algorithms are known as deep-learning-based methods. A detailed discussion of the algorithms is presented in Farhadmanesh et al ( 46 ). The following explains each method needed to reproduce the results.…”

Section: Computer Visionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Video-Based Air Traffic Surveillance System for Counting General Aviation Aircraft Operations at Non-Towered Airports

Farhadmanesh

Marković

Rashidi

2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

The vast majority of U.S. airports are not equipped with control towers, which limits their ability to keep records of flight operations. Attempts have been made to use sensor-based technologies to count aircraft operations at non-towered airports; however, they exhibit limited accuracy. To this end, we developed an automated video-based surveillance system capable of detecting general aviation aircraft departure and landing operations, which comprise the vast majority of operations at non-towered airports. The proposed computer vision method is comprised of three modules: aircraft detection, aircraft tracking, and operations count and classification. We explored different camera layouts and state-of-the-art machine learning and deep learning methods to determine the best settings to extract operations trajectory features for operations count and classification. The proposed method was tested at five non-towered airports. Integrating deep-neural-network-based aircraft detectors and image-correlation-based aircraft trackers achieved an accuracy of about 95%, while ensuring processing times that are needed for real-time implementation.

show abstract

Implementing Convolution Neural Network (CNN) Based Approach for Traffic Queue Length and Delay Estimation at Highway Ramps

Tiwari,

Rashidi,

Marković

2024

Computing in Civil Engineering 2023

View full text Add to dashboard Cite

Implementing Haar Cascade Classifiers for Automated Rapid Detection of Light Aircraft at Local Airports

Cited by 4 publications

References 9 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

Automated Video-Based Air Traffic Surveillance System for Counting General Aviation Aircraft Operations at Non-Towered Airports

Implementing Convolution Neural Network (CNN) Based Approach for Traffic Queue Length and Delay Estimation at Highway Ramps

Contact Info

Product

Resources

About