What question did this study set out to answer?

Evaluate and compare the performance of YOLOv7, YOLOv8, and RT-DETR object detection architectures for identifying military aircraft in complex visual environments.

January 31, 2025Bilişim Teknolojileri DergisiOpen Access

Detection of Military Aircraft Using YOLO and Transformer-Based Object Detection Models in Complex Environments

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Evaluate and compare the performance of YOLOv7, YOLOv8, and RT-DETR object detection architectures for identifying military aircraft in complex visual environments.
Assessed models on a benchmark dataset comprising 19,514 images spanning 43 military aircraft models across urban, rural, and coastal backgrounds from varying angles.
Optimized model hyperparameters to mitigate class imbalance issues observed across different aircraft categories.
YOLOv8 achieved the highest overall detection precision, reaching 94% mean Average Precision (mAP) and 88.1% recall.
RT-DETR demonstrated the highest sensitivity, yielding 92.7% mAP and 90.4% recall across diverse visual backgrounds.
YOLOv7 attained 90.2% mAP and 82.7% recall on the evaluation dataset.

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Implication

Comparative evaluation reveals high military aircraft detection accuracy across diverse environments using YOLOv8 and RT-DETR, highlighting viable architectures for automated air defense systems.

Cite This Study

Şengül et al. (2025) studied this question.

synapsesocial.com/papers/6a73dbd90375516f85f15f6e https://doi.org/10.17671/gazibtd.1549034

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Implication

Comparative evaluation reveals high military aircraft detection accuracy across diverse environments using YOLOv8 and RT-DETR, highlighting viable architectures for automated air defense systems.

Detection of Military Aircraft Using YOLO and Transformer-Based Object Detection Models in Complex Environments

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Implication

Cite This Study

Detection of Military Aircraft Using YOLO and Transformer-Based Object Detection Models in Complex Environments

Key Points

Discussion

Implication

Cite This Study

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