# Estimation of Wind Turbine Blade Icing Volume Based on Binocular Vision
###### Abstract
[24] combined the Sum of Absolute Differences (SAD) with gradient information, using the absolute sum of the gradient values of the matching points and the absolute gray-level differences within the support window as the basis for the matching cost. [PERSON] et al. [25] developed a real-time high-quality stereo vision system based on a Field Programmable Gate Array (FPGA), combining AD-Census cost initialization, cross-based aggregation, and semi-global optimization to achieve high-quality depth results on FPGA. The system can flexibly adjust image resolution, parallelism, and support region size for optimal efficiency. [PERSON] et al. [26] proposed a non-local cost aggregation algorithm for stereo matching based on a cross-tree structure, edges, and superpixel priors. By performing non-local cost aggregation through the cross-structure of horizontal and vertical trees and combining edge and superpixel priors, it effectively avoids erroneous aggregation across depth boundaries. [PERSON] et al. [27] introduced an improved Census transform algorithm for noise-robust stereo matching, enhancing matching accuracy and robustness by symmetrically comparing pixel intensities separated by a certain distance within the matching window. [PERSON] et al. [28] converted color images into the HSV (hue, saturation, value) space, established a hue absolute difference cost calculation function, and fused it with the Census cost. [PERSON] et al. [29] proposed a multi-feature fusion stereo matching algorithm based on an improved Census transform, enhancing matching accuracy and noise resistance by introducing gradient cost, edge, and feature point information during the cost computation stage. [PERSON] et al. [30] proposed a stereo matching algorithm based on an improved Census transform, enhancing robustness to noise by introducing weighted average computation to replace the central pixel in the traditional Census transform. Based on the aforementioned studies, this paper proposes a stereo matching algorithm that employs dynamic windows combined with multiple features and reordering. Gradient, color, and other information are fused to generate the final matching cost. The cost is aggregated using a cross-based support region method. Finally, a Winner-Take-All (WTA) method [31] and multi-step optimization are used to generate the final disparity map. Subsequently, image processing techniques combined with 3D reconstruction methods are used to model the geometric shape of the ice, and its volume is estimated. The proposed method aims to overcome the limitations of existing methods, providing a non-contact, accurate, and efficient solution for ice volume estimation. The remainder of this paper is organized as follows: Section 2 provides a detailed description of the binocular vision-based method, including computation methods for stereo matching and volume calculation. Section 3 presents the results and validations of the proposed method under various experimental conditions. Finally, Section 4 discusses the significance of the research findings and proposes future research directions.