Over 97% of the US airports are not equipped with control towers, which limits their ability to keep records of flight operations. This problem can be alleviated with implementation of an automated video-based air traffic surveillance system capable of detecting light aircraft operations, which comprise the vast majority of operations at non-towered airports. To this end, we propose a rapid automatic light-aircraft detection method based on Haar cascade classifiers. The video data are collected from three public-use Utah airports and used for detecting both arrival and departure operations of light aircraft. The proposed model is compared with two state-of-the-art deep neural networks—SSD and YOLO—which typically achieve high accuracy at the cost of decreased speed. The Haar cascade provides competitive accuracy at a much-reduced processing time, which makes the proposed method suitable for real-time applications.
Implementing Haar Cascade Classifiers for Automated Rapid Detection of Light Aircraft at Local Airports
ASCE International Conference on Computing in Civil Engineering 2021 ; 2022 ; Orlando, Florida
2022-05-24
Conference paper
Electronic Resource
English
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