A real-time photoelectric system for foreign objects debris detection on airport runways

Ye, Demao; Li, Peizheng; Zhang, Haiyang

doi:10.1117/12.2627747

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“…In today's society with rapid economic and technological development, traveling by fast and comfortable civil airliners has become more consumers' first choice [1][2]. The frequency of largescale aircraft use has caused people to pay attention to the safety of aircraft operation and the presence of foreign objects on the airport runway, which can cause the aircraft to fail to take off normally or even run off the airport runway and cause dangerous situations such as airframe explosions, has become one of the safety issues of great concern [3][4]. The maximum aircraft speed on the airport runway before takeoff and after landing can reach 700km/h.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based foreign object detection method for aviation runways

Wang

2023

Applied Mathematics and Nonlinear Sciences

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Airport runway foreign object detection systems can quickly and accurately detect and identify foreign runway objects, which is significant for ensuring airport flight safety. Because of the drawbacks shown by the algorithm, the paper proposes to combine a new system scheme based on deep learning to obtain multiple feature information for identifying foreign objects on airport runways and improve the recognition accuracy of foreign object detection. This paper designs and constructs a dataset for accomplishing airport runway foreign object detection based on the data distribution and attribute semantics of actual airport runway foreign object scenarios and the technical features of deep learning, designs FOD detection and multi-attribute recognition networks, further design algorithms, and perform validation. The results show that the deep learning technology can accomplish all tasks of the airport runway foreign object detection system, which has not only good robustness to different environments but also has practical value for multi-tasking, and the localization task can accurately obtain the location information of foreign objects and improve the recognition accuracy of foreign object detection. Therefore, the deep learning-based airport runway foreign object recognition system designed in this paper is effective and can improve the accuracy of foreign object recognition.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based foreign object detection method for aviation runways

Wang

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

show abstract

A real-time photoelectric system for foreign objects debris detection on airport runways

Cited by 1 publication

References 1 publication

Deep learning-based foreign object detection method for aviation runways

Deep learning-based foreign object detection method for aviation runways

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