OWT-DNet: A Timely and High-Accuracy End-to-End Offshore Wind Turbine Detection Network Based on Multimodal Remote Sensing Data

With the rapid development of the offshore wind power industry in recent years, its effects on local social, economic and ecological environments have attracted widespread attention. Therefore, a timely understanding of the development status of offshore wind power, specifically, offshore wind turbines (OWTs) is crucial for the healthy and sustainable development of the offshore wind power industry. However, existing OWT detection methods often struggle to achieve timely, high-precision end-to-end detection of OWTs. To address this, in this study, an OWT detection network (OWT-DNet) based on multimodal remote sensing data is proposed. This network integrates Sentinel-1 synthetic aperture radar (SAR) imagery and Sentinel-2 optical imagery, effectively addressing the insufficient semantic information inherent in single-modal data for OWT detection. Experiments across five global test regions demonstrate that OWT-DNet achieves detection accuracy, recall, and comprehensive evaluation metrics that exceed 99.9%. Furthermore, OWT-DNet demonstrates outstanding detection performance under complex weather conditions. Comparative and ablation experiments validate the network's superior capability in OWT detection tasks. Overall, timely, high-precision end-to-end OWT detection is achieved for the first time on the basis of multimodal remote sensing data. Furthermore, an inaugural multimodal OWT sample dataset is established, laying a solid foundation for future OWT detection research.