Version 1
: Received: 7 February 2024 / Approved: 7 February 2024 / Online: 7 February 2024 (12:55:29 CET)
How to cite:
Khan Mohammadi, M.; Schneidereit, T.; Mansouri Yarahmadi, A.; Breuß, M. Investigating training datasets of real and synthetic images for swimmer localisation with YOLO. Preprints2024, 2024020446. https://doi.org/10.20944/preprints202402.0446.v1
Khan Mohammadi, M.; Schneidereit, T.; Mansouri Yarahmadi, A.; Breuß, M. Investigating training datasets of real and synthetic images for swimmer localisation with YOLO. Preprints 2024, 2024020446. https://doi.org/10.20944/preprints202402.0446.v1
Khan Mohammadi, M.; Schneidereit, T.; Mansouri Yarahmadi, A.; Breuß, M. Investigating training datasets of real and synthetic images for swimmer localisation with YOLO. Preprints2024, 2024020446. https://doi.org/10.20944/preprints202402.0446.v1
APA Style
Khan Mohammadi, M., Schneidereit, T., Mansouri Yarahmadi, A., & Breuß, M. (2024). Investigating training datasets of real and synthetic images for swimmer localisation with YOLO. Preprints. https://doi.org/10.20944/preprints202402.0446.v1
Chicago/Turabian Style
Khan Mohammadi, M., Ashkan Mansouri Yarahmadi and Michael Breuß. 2024 "Investigating training datasets of real and synthetic images for swimmer localisation with YOLO" Preprints. https://doi.org/10.20944/preprints202402.0446.v1
Abstract
In this paper we develop and explore a methodical pipeline for swimmer localisation in outdoor environments. The developed framework is intended to be used for enhancing swimmer safety. A main issue we deal with by the proposed approach is the lack of real world training data in such outdoor environments. Natural lighting changes, dynamic water textures and possibly barely visible swimming persons are key elements to approach. We account for these difficulties by adopting an effective background removal technique with available training data. This allows us to edit swimmers into natural environment backgrounds for the use in subsequent image augmentation. We created 17 training datasets with real images, synthetic images and a mixture of both to investigate different aspects and characteristics of the proposed approach. The datasets are used to train a YOLO architecture for the possible future application in real-time detection. The trained framework is then tested and evaluated on outdoor environment imagery acquired by a safety drone to investigate and confirm the usefulness for outdoor swimmer localisation.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
