Runway vs. Taxiway: Challenges in Automated Line Identification and Notation Approaches

TL;DR

This paper examines the difficulties in automatically identifying runway and taxiway markings, modifies existing algorithms, and proposes a CNN-based classification approach to improve robustness for autonomous aviation safety.

Contribution

It introduces AssistNet, a CNN-based classification step, to enhance the accuracy and reliability of runway and taxiway marking detection in complex environments.

Findings

Modified ALINA with limited success

Identified environmental challenges in marking detection

Proposed CNN-based AssistNet for improved robustness

Abstract

The increasing complexity of autonomous systems has amplified the need for accurate and reliable labeling of runway and taxiway markings to ensure operational safety. Precise detection and labeling of these markings are critical for tasks such as navigation, landing assistance, and ground control automation. Existing labeling algorithms, like the Automated Line Identification and Notation Algorithm (ALINA), have demonstrated success in identifying taxiway markings but encounter significant challenges when applied to runway markings. This limitation arises due to notable differences in line characteristics, environmental context, and interference from elements such as shadows, tire marks, and varying surface conditions. To address these challenges, we modified ALINA by adjusting color thresholds and refining region of interest (ROI) selection to better suit runway-specific contexts. While these modifications yielded limited improvements, the algorithm still struggled with consistent runway identification, often mislabeling elements such as the horizon or non-relevant background features. This highlighted the need for a more robust solution capable of adapting to diverse visual interferences. In this paper, we propose integrating a classification step using a Convolutional Neural Network (CNN) named AssistNet. By incorporating this classification step, the detection pipeline becomes more resilient to environmental variations and misclassifications. This work not only identifies the challenges but also outlines solutions, paving the way for improved automated labeling techniques essential for autonomous aviation systems.