Mitigation of runway incursions by using a convolutional neural network to detect and identify airport signs and markings

Zhi Hao Chen, Jyh Ching Juang

研究成果: Article

摘要

Runway incursions have resulted in incidents, confusions, and delays in airport operation. With the aim of reducing the risk of runway incursions, in this work, we investigate the use of a machine learning (ML) approach to detect and identify airport signs and markings to enhance operational safety especially in a low-visibility scenario. An artificial intelligence (AI) sensor for detecting the pixels developed and modeled using a convolutional neural network (CNN) is developed. In this design, the neural network outputs the feature vector model after the convolution operation. A filter is used to detect the pixels of the background image of the airport environment. The weight of the feature object is then added with a maximum pool layer after a convolution layer to find the feature map. The CNN is trained to demonstrate its capability in performing object detection and identification. It is expected that the proposed approach can be used to enhance airport operational safety and mitigate the risk of runway incursion.

原文English
頁(從 - 到)3947-3958
頁數12
期刊Sensors and Materials
31
發行號12
DOIs
出版狀態Published - 2019 一月 1

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Materials Science(all)

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