Enhancing Offshore Wind Turbine Inspection with YoloMos Cloud-based UAV Visual Processing

With the increasing demand for offshore wind turbine inspections and the challenges posed by complex marine environments to inspection efficiency and safety, this paper proposes an innovative YoloMos aerial and underwater UAV visual inspection system. The system integrates the YOLO deep learning algorithm and the MOSSE tracking filter to address the limitations of existing inspection technologies in real-time performance and environmental adaptability. Featuring autonomous flight capabilities, high-performance imaging equipment, and intelligent data processing modules, the YoloMos system enables real-time acquisition and analysis of image data from wind turbines and their surrounding environments. By effectively combining YOLO detection with MOSSE tracking, the system achieves efficient and precise target detection and tracking in complex environments. Experimental results demonstrate that the YoloMos system excels in detection accuracy and real-time performance, significantly reducing reliance on manual operations. This research showcases a promising application prospect for offshore wind farm inspections and has the potential to advance the intelligent application of UAV technologies in power inspection, marine monitoring, and other domains.