Enhancing Cyclone Center Identification in Radar Images through Deep Learning and Match Recognition

• Published in: 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) pp. 1 - 6
• Main Authors: Shanmugam, Thirumurugan, Chandrashekar, Chirag, Subburaj, Maheswari, Sivaraman, Arun Kumar, Sultana, Ajmery, Thangavel, Senthil Kumar
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.06.2024
• Subjects: Accuracy; Adaptation models; Cyclone detection; Cyclones; Deep learning; efficient det and YOLO model; Machine learning algorithms; Radar; Radar imaging; Training; Transfer learning; YOLO
• ISSN: 2473-7674
• DOI: 10.1109/ICCCNT61001.2024.10724488

This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone's center is vital for predicting its intensity and trajectory. However, challenges persist in automatically locating the center due to the diverse nature of cyclone morphology and structure. To address this, the deep convolutional network's capability is leveraged to capture various structural features in images by proposing two-step approach for cyclone center localization. Initially, a pre-trained EfficientDet model is employed using transfer learning to obtain weights. Subsequently, these weights, along with the data, are utilized in a deep learning model to provide precise coordinates of the cyclone center in the respective image. The effectiveness of existing deep learning and machine learning models show that the cyclone prediction systems have an accuracy ranging from 86 \% to 92 \% or better, and cyclone eye detection accuracy surpassing 87%. Experiment outcomes indicate that the proposed methodology outperforms conventional methods and existing works, showcasing its potential for enhancing cyclone monitoring and forecasting.