A Recurrent Approach for Uninterrupted Tracking of Rotor Blades Using Kalman Filter

• Published in: IEEE access 2024, Vol.12, p.5321-5332
• Main Authors: Xu, Yiming, Fu, Zhenyu, Peng, Wei, Ding, Ziheng, Lu, Guan, Liu, Qiang
• Format: Article
• Language: English
• Subjects: Algorithms; Computer networks; Field of view; Kalman filters; Maintenance; motion estimation; Object recognition; Optical tracking; Rotor blades; Rotor blades (turbomachinery); Target tracking; Turbine blades; Unmanned aerial vehicles; unmanned inspection; Visual aspects; wind turbine; Wind turbines; YOLOv5
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3344805

With the escalating requirements for maintenance of wind turbines, the deployment of Unmanned Aerial Vehicles (UAVs) for inspection tasks has become increasingly prevalent. However, wind turbine blades, which are thin and long, possess weak texture features that lead to target confusion when tracking specific parts of the dynamic blades. Additionally, wind turbine units, being large dynamic structures, often exceed the camera’s field of view (FOV) and exhibit unique motion characteristics. These factors make the visual tracking of specific components unstable due to the lack of global motion information. In order to address the aforementioned challenges and achieve consistent calibration of key components under the dynamic operating conditions of wind turbines, this study has adopted a strategy of integrating the Squeeze-and-Excitation Network (SEnet) into the backbone network of YOLOv5. Innovatively, two hyperparameters have been introduced into the existing loss function to adjust the weights of samples under conditions of data imbalance, thereby enhancing the performance of the detection model. In the application of the DeepSORT tracking algorithm, Long Short-Term Memory (LSTM) networks have been combined to predict the trajectory of the rotor blade’s central point, and an optimized Kalman filter has been employed to significantly improve the system’s adaptability and precision under various motion conditions. Empirical results from this study underscore the efficacy of the proposed method, demonstrating its capability to accurately differentiate individual blades as well as specific blade segments. Compared to the traditional YOLOv5, the enhanced YOLOv5-SE has demonstrated a 5.3% improvement in the Mean Average Precision (mAP_0.5) evaluation metric. Moreover, the improved DeepSORT algorithm has exhibited high efficiency in maintaining continuous and stable tracking of moving blades, adeptly handling scenarios where rotor blades frequently enter and exit the FOV. This advancement paves the way for the broader application of UAVs in wind turbine inspections, offering the potential for more efficient and accurate maintenance protocols.