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.

提出一种新的注释框架 ALINA 用于标注出租车道数据集，并使用其生成对应的像素坐标标注，从而在测试中显示了高达 98.45% 的检测率。