# Estimation of Wind Turbine Blade Icing Volume Based on Binocular Vision
###### Abstract  
Icing on wind turbine blades in cold and humid weather has become a detrimental factor limiting their efficient operation, and traditional methods for detecting blade icing have various limitations. Therefore, this paper proposes a non-contact ice volume estimation method based on binocular vision and improved image processing algorithms. The method employs a stereo matching algorithm that combines dynamic windows, multi-feature fusion, and reordering, integrating gradient, color, and other information to generate matching costs. It utilizes a cross-based support region for cost aggregation and generates the final disparity map through a Winner-Take-All (WTA) strategy and multi-step optimization. Subsequently, combining image processing techniques and three-dimensional reconstruction methods, the geometric shape of the ice is modeled, and its volume is estimated using numerical integration methods. Experimental results on volume estimation show that for ice blocks with regular shapes, the errors between the measured and actual volumes are 5.28%, 8.35%, and 4.85%, respectively; for simulated icing on wind turbine blades, the errors are 5.06%, 6.45%, and 9.54%, respectively. The results indicate that the volume measurement errors under various conditions are all within 10%, meeting the experimental accuracy requirements for measuring the volume of ice accumulation on wind turbine blades. This method provides an accurate and efficient solution for detecting blade icing without the need to modify the blades, making it suitable for wind turbines already in operation. However, in practical applications, it may be necessary to consider the impact of illumination and environmental changes on visual measurements. wind turbine; blade icing; binocular vision; stereo matching; volume estimation 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., pp. 2010-2010,  
small-scale, or unevenly distributed icing is relatively low. Furthermore, it is susceptible to disturbances from nacelle vibrations and wind speed variations during operation, which can affect its detection accuracy. [PERSON] et al. [7] demonstrated that ultrasonic imaging technology performs well in detecting thick ice layers and large blade structures. However, its sensitivity decreases for thin ice layers, and its performance is further reduced when the blade surface roughness increases, limiting its application under complex surface conditions. [PERSON] et al. [8] adopted ultrasonic guided waves combined with Principal Component Analysis (PCA) to eliminate the influence of temperature, achieving high-precision ice monitoring. Nonetheless, the deployment costs are high, requiring multiple ultrasonic sensors installed on the blade, which are prone to environmental noise and blade vibration, thereby reducing detection stability. [PERSON] et al. [9] proposed a low-cost detection method based on a heated cylindrical probe, which can effectively monitor the onset and intensity of icing. However, it is sensitive to wind speed and temperature in extreme weather conditions, potentially resulting in errors. In recent years, significant progress has been made in wind turbine blade icing detection technologies driven by data-driven and intelligent methods. [PERSON] et al. [10] employed a particle swarm optimization algorithm combined with a support vector machine to predict blade icing conditions, achieving high accuracy. However, this approach requires extensive data preprocessing, and its generalizability across different wind farms remains to be validated. [PERSON] et al. [11] proposed the 1D-CNN-SBiGRU model, which achieves high-precision predictions through feature selection and deep learning, exhibiting strong anti-interference capability. However, the computational complexity of this model may affect real-time performance. [PERSON] et al.