ALINA: Advanced Line Identification and Notation Algorithm

TL;DR

ALINA is a novel annotation framework for labeling taxiway datasets with high accuracy, utilizing the CIRCLEDAT algorithm for pixel detection, significantly reducing manual effort and achieving a 98.45% detection rate.

Contribution

The paper introduces ALINA and CIRCLEDAT algorithms for efficient, accurate labeling of taxiway images across various conditions, improving over traditional manual methods.

Findings

Labeled 60,249 frames from taxiway dataset using ALINA.

Achieved 98.45% detection rate with the CBEM set.

Demonstrated effectiveness across different weather and perspectives.

Abstract

Labels are the cornerstone of supervised machine learning algorithms. Most visual recognition methods are fully supervised, using bounding boxes or pixel-wise segmentations for object localization. Traditional labeling methods, such as crowd-sourcing, are prohibitive due to cost, data privacy, amount of time, and potential errors on large datasets. To address these issues, we propose a novel annotation framework, Advanced Line Identification and Notation Algorithm (ALINA), which can be used for labeling taxiway datasets that consist of different camera perspectives and variable weather attributes (sunny and cloudy). Additionally, the CIRCular threshoLd pixEl Discovery And Traversal (CIRCLEDAT) algorithm has been proposed, which is an integral step in determining the pixels corresponding to taxiway line markings. Once the pixels are identified, ALINA generates corresponding pixel coordinate annotations on the frame. Using this approach, 60,249 frames from the taxiway dataset, AssistTaxi have been labeled. To evaluate the performance, a context-based edge map (CBEM) set was generated manually based on edge features and connectivity. The detection rate after testing the annotated labels with the CBEM set was recorded as 98.45%, attesting its dependability and effectiveness.