ALINA: Advanced Line Identification and Notation Algorithm

Mohammed Abdul Hafeez Khan, Parth Ganeriwala, Siddhartha Bhattacharyya, Natasha Neogi, Raja Muthalagu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 7293-7302

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 60249 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.