为叶片分割设计可学习的颜色空间，提升分割精度。
Discriminant Learning-based Colorspace for Blade Segmentation
- 基于判别分析构建可学习的颜色表示，优化类间区分度。
- 在风力涡轮机叶片数据上实现显著精度提升，验证方法有效性。
- 适合需要高精度图像分割的工业检测场景使用。
不合适的颜色表征常阻碍精确的图像分割，但许多现代算法忽视了这一关键预处理步骤。本文提出一种新型多维非线性判别分析算法——颜色空间判别分析（CSDA），以改善分割效果。该方法将线性判别分析拓展至深度学习框架，通过广义判别损失最大化多维有符号类间可分性，同时最小化类内差异，从而定制化颜色表示。为确保训练稳定，引入三种替代损失函数，实现判别颜色空间与分割过程的端到端联合优化。在风力涡轮机叶片数据上的实验表明，该方法带来显著的精度提升，凸显了领域特定预处理的重要性。
原文摘要 · Abstract (English)
Suboptimal color representation often hinders accurate image segmentation, yet many modern algorithms neglect this critical preprocessing step. This work presents a novel multidimensional nonlinear discriminant analysis algorithm, Colorspace Discriminant Analysis (CSDA), for improved segmentation. Extending Linear Discriminant Analysis into a deep learning context, CSDA customizes color representation by maximizing multidimensional signed inter-class separability while minimizing intra-class variability through a generalized discriminative loss. To ensure stable training, we introduce three alternative losses that enable end-to-end optimization of both the discriminative colorspace and segmentation process. Experiments on wind turbine blade data demonstrate significant accuracy gains, emphasizing the importance of tailored preprocessing in domain-specific segmentation.
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