Deep learning-based foreign object detection method for aviation runways

Abstract

Abstract 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.