Active Learning for Railway Semantic Segmentation through Ant Colony Optimization

In autonomous driving, a tremendous amount of imagery data is collected at all times. Manual annotation of such high-resolution images represents a costly and inefficient process. Active Learning comes to aid annotators in their process to focus on labelling meaningful samples which leads to competitive Machine Learning models, useful for various prediction tasks. In this paper, the authors introduce a novel Active Learning sampling technique, inspired by the Ant Colony Optimization algorithm, that considers both uncertainty and diversity features. The authors also introduce two hybrid sampling techniques that use weighted sums. The authors validate the proposed method on the Semantic Segmentation task, on a popular dataset from the railway domain. The authors also showcase the effectiveness of Active Learning in the scenario of Rail Semantic Segmentation by using only a quarter of the data to obtain competitive results of up to 78% mean Intersection over Union.

• Record URL:
  • https://doi.org/10.1016/j.procs.2024.09.491
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1877050924025365
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18770509
• Supplemental Notes:
  • © 2024 The Author(s). Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Alexandrescu, Andrei-Robert
  • Dioşan, Laura
• Conference:
  • 28th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES 2024)
  • Location: Seville , Spain
  • Date: 2024-9-11 to 2024-9-13
• Publication Date: 2024

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 724-733
• Serial:
  • Procedia Computer Science
  • Volume: 246
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1877-0509
  • Serial URL: http://www.sciencedirect.com/science/journal/18770509
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Algorithms; Autonomous vehicles; Detection and identification systems; Labeling; Machine learning; Mathematical programming; Optimization; Railroads
• Subject Areas: Data and Information Technology; Railroads; Vehicles and Equipment;

Filing Info

• Accession Number: 01944263
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 28 2025 2:52PM