A Comparative Analysis of YOLOv8 and YOLOv11 for Tropical Cyclone Detection: The Impact of Data Augmentation on Model Performance

Gupta, Punit, Chaurasiya, Liza, Uchat, Vyom Parag and Gupta, Manisha (2026) A Comparative Analysis of YOLOv8 and YOLOv11 for Tropical Cyclone Detection: The Impact of Data Augmentation on Model Performance. In: Proceedings of International Conference on Recent Innovations in Computing. ICRIC 2024. Lecture Notes in Electrical Engineering (1648). Springer, Singapore, Budapest, Hungary, pp. 218-227. ISBN 978-981-92-1029-9

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Abstract

Tropical cyclones, also known as TCs, are a category of severe weather events considered to have a significant weather impact, making it extremely important to detect them accurately. Despite the advantages presented by deep-learning techniques compared to the usual forecasting approaches, the use of the best model is still a concern. This study is a comparative analysis of the performance between two state-of-the-art, light-weight models, namely YOLOv8n and YOLOv11n, on the detection task of tropical cyclones. Four experiments of the above-mentioned models were conducted on the INCYDE satellite images, using a normal unaugment dataset compared to an augmented normal dataset, applying standard geometric transformation techniques, namely flipping and rotation. The experimental results demonstrate a significant improvement on the YOLOv11n model, using the unaugment dataset, resulting in a mAP@.50–.95 score of 0.921 and a detection rate of 0.994. Unexpectedly, the standard augmentation approach deteriorated the performance on both architectures.

Item Type:	Book Section
Uncontrolled Keywords:	Data Augmentation; Object Detection; Satellite Imagery; Tropical Cyclone Detection; YOLOv11; YOLOv8
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning G Geography. Anthropology. Recreation > GE Environmental Sciences > Earth sciences > Atmospheric science > Meteorology > Weather > Storms
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	22 Sep 2026 13:13
Last Modified:	22 Sep 2026 13:13
URI:	https://norma.ncirl.ie/id/eprint/9938

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