Detection of Fighter Planes in Aerial Images using YOLO V8

Lekkala, Santosh Kumar Reddy (2023) Detection of Fighter Planes in Aerial Images using YOLO V8. Masters thesis, Dublin, National College of Ireland.

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Abstract

This research explores the efficacy of YOLO v8 in detecting fighter planes in aerial images, crucial for military defense. YOLO v8 strikes a balance between speed and accuracy, making it ideal for real-time applications. The study evaluates its performance against previous versions, addressing challenges in identifying small targets amidst clutter. Using a large dataset and YOLO v8, the mean Average Precision (mAP) achieved 70 percent, indicating the potential for improved accuracy. However, computational constraints hampered completion, implying unrealized potential with more data and training. The achievement of 54 percent mAP in a difficult situation with 35 lessons emphasizes the need of thorough training. The study predicts the significance of YOLO v8 in transforming military aerial surveillance, highlighting the critical balance of precision and speed in fighter aircraft identification. For realizing potential of YOLO v8, in depth research could entail amalgamation of added fighter planes intense data training.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Horn, Christian UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science U Military Science > U Military Science (General) Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence > Computer vision Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence > Computer vision
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	15 May 2025 16:12
Last Modified:	15 May 2025 16:12
URI:	https://norma.ncirl.ie/id/eprint/7557

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