# Estimation of Wind Turbine Blade Icing Volume Based on Binocular Vision
###### Abstract
[12] introduced the IcingFL model within a federated learning framework, which achieves high-accuracy icing detection and data privacy protection through local model training and global model aggregation. However, federated learning entails high communication costs. These studies indicate that wind turbine blade icing detection is transitioning from traditional methods to data-driven and intelligent optimization approaches, with future advancements needed in real-time performance, cost-effectiveness, and adaptability. [PERSON] et al. [13] proposed a unified semi-supervised contrastive learning framework, which integrates supervised and unsupervised learning to address the challenges of scarce labeled data and class imbalance. This method demonstrates strong robustness and generalization across multiple datasets. [PERSON] et al. [14] proposed a method that combines a spatiotemporal attention mechanism with an adaptive weight loss function. By extracting spatiotemporal features from SCADA data and dynamically adjusting class weights, this approach effectively addresses data imbalance issues. However, the model's training relies heavily on high computational resources. [PERSON] et al. [15] developed a blade icing monitoring and de-icing device based on machine vision. By integrating laser sensors and ultrasonic de-icing technology, this approach achieves a balance between real-time performance and precision. Although the technology performs well on small-scale wind turbines, its adaptability to extreme environments still requires optimization. With advances in imaging technology and computational algorithms, computer vision offers a promising alternative for non-contact detection of blade icing [16]. In particular, binocular vision technology can reconstruct three-dimensional structures from two-dimensional images taken from slightly different viewpoints. This technology has been successfully applied in various fields such as 3D reconstruction [17], robotic navigation [18], autonomous driving and environmental monitoring [19]. In the detection of blade icing on wind turbines, employing binocular vision to capture images of blade icing and combining image processing techniques to extract and analyze information is a feasible detection method. This approach can measure ice thickness without modifying the blades, making it suitable for wind turbines already in operation. Inspections can be conducted via drones or robots, further achieving unmanned operation. Moreover, the detection results are intuitive, facilitating subsequent processing by staff. This binocular vision-based scheme provides a new reliable path for blade icing detection on wind turbines. To further enhance the detection performance of binocular vision systems, selecting an appropriate image processing algorithm is particularly important. The Census transform, known for its robustness to illumination changes, fast execution speed, and ease of implementation, has been widely used in real-time 3D measurement and other image processing tasks. However, this algorithm also has drawbacks, such as dependence on the central pixel, sensitivity to noise, and poor performance in weakly textured regions [20]. Addressing these issues, [PERSON] et al. [21] proposed a stereo matching algorithm combining gradient similarity and adaptive support weights, improving matching accuracy by replacing the traditional intensity similarity measure. [PERSON] et al. [22] proposed a fusion method based on Absolute Difference (AD) and Census cost, compensating for the shortcomings of single-cost approaches. By combining the AD-Census transform with improved cross-region support and utilizing a scanline optimization framework, their algorithm demonstrated excellent performance in the Middlebury benchmark tests. [PERSON] et al. [23] introduced an efficient one-shot local disparity estimation algorithm, utilizing a trimodal cross Census transform and advanced support weights to enhance the accuracy of disparity maps, extending it to video disparity estimation. [PERSON] et al.