Mitigation of Runway Incursions by Using a Convolutional Neural Network to Detect and Identify Airport Signs and Markings

Abstract

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.

Toward Certification of Machine-Learning Systems for Low Criticality Airborne Applications

“…• object detection and classification, based upon camera inputs, for example, the detection of runway debris [4] and objects [5], of airport signs, or for the avoidance of runway incursions [6].…”

Improving Aviation Safety through Modeling Accident Risk Assessment of Runway

“…These accidents occur due to the improper positioning of a plane, vehicle, or human in a protected area for landing and take-off [ 15 , 16 ]. In other words, RI is an accident that occurs when a plane crashes into another plane or vehicle, or human at the runway.…”