Automatic Detection of Wind Turbine Blade Surface Cracks Based on UAV-Taken Images

作者：Wang, Long; Zhang, Zijun^*

来源：IEEE Transactions on Industrial Electronics, 2017, 64(9): 7293-7303.

DOI：10.1109/TIE.2017.2682037

• Blade image
• crack detection
• data-driven model
• Haar-like features
• wind turbine (WT)

In this paper, a data-driven framework is proposed to automatically detect wind turbine blade surface cracks based on images taken by unmanned aerial vehicles (UAVs). Haar-like features are applied to depict crack regions and train a cascading classifier for detecting cracks. Two sets of Haar-like features, the original and extended Haar-like features, are utilized. Based on selected Haar-like features, an extended cascading classifier is developed to perform the crack detection through stage classifiers selected from a set of base models, the LogitBoost, Decision Tree, and Support Vector Machine. In the detection, a scalable scanning window is applied to locate crack regions based on developed cascading classifiers using the extended feature set. The effectiveness of the proposed data-driven crack detection framework is validated by both UAV-taken images collected from a commercial wind farm and artificially generated. The extended cascading classifier is compared with a cascading classifier developed by the LogitBoost only to show its advantages in the imagebased crack detection. A computational study is performed to further demonstrate the success of the proposed framework in identifying the number of cracks and locating them in original images.

• 出版日期2017-9
• 单位香港城市大学