Agus Nursalam Kitono, Khairun Saddami, Yunida Yunida, Teuku Yuliar Arif, Hubbul Walidainy
Radio frequency interference (RFI) in weather radar systems frequently leads to image distortions, undermining the accuracy of meteorological analyses. Currently, interference detection relies on manual inspection by human operators, which poses challenges in terms of consistency, speed, and scalability. This study focuses on the implementation of multiple You Only Look Once version 12 (YOLOv12) variants and compares their performance with the real-time detection transformer (RT-DETR) model from the Ultralytics library for automatic detection tasks. While most previous research has employed foreign weather radar datasets, these may not be compatible with the spectral characteristics and operational conditions of radar systems in Indonesia. To address this gap, the study focuses on developing and leveraging a representative weather radar image dataset tailored to the Indonesian context, including examples of RFI and clean radar imagery. The deep learning models were trained on this dataset to enhance detection accuracy and improve adaptation to local radar environments. Model performances were evaluated using precision, recall, mean average precision (mAP), F1-score, and computational efficiency to identify the most effective approach for detecting RFI. Results show that the YOLOv12 models outperform RT-DETR in detecting RFI on weather radar imagery. The YOLOv12x variant achieved the highest accuracy of 0.828 (mAP@0.5), while YOLOv12m and YOLOv12l offered a balanced trade-off between accuracy and efficiency, indicating that YOLOv12 provides a more reliable and computationally efficient solution for operational weather radar monitoring. ©2025 IEEE.
Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia
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