用深度网络优化判别分析，提升风力叶片分割精度与稳定性。
Probabilistic Deep Discriminant Analysis for Wind Blade Segmentation
- 引入概率化判别分析，通过深度网络直接优化类间分离性。
- 在风力叶片分割任务中实现更低类内方差和更高预测置信度。
- 适合需要高精度图像分割的工业维护场景，如风电设备检测。
线性判别分析虽能增强类别可分性，但在非线性可分数据上表现不佳。为此，我们提出深度判别分析（DDA），利用深度网络直接优化Fisher准则。为确保训练稳定并避免计算不稳定性，引入有符号类间方差，使用sigmoid函数约束输出，并将乘法关系转化为加法关系。我们设计了两种稳定的DDA损失函数，并结合概率损失，形成概率化判别分析（PDDA）。PDDA有效降低输出分布中的类别重叠，显著减少类内方差，生成高置信度预测。在风力叶片分割任务中，PDDA展现出性能与一致性的显著提升，对风能设施维护至关重要。据我们所知，这是首个将DDA应用于图像分割的工作。
原文摘要 · Abstract (English)
Linear discriminant analysis improves class separability but struggles with non-linearly separable data. To overcome this, we introduce Deep Discriminant Analysis (DDA), which directly optimizes the Fisher criterion utilizing deep networks. To ensure stable training and avoid computational instabilities, we incorporate signed between-class variance, bound outputs with a sigmoid function, and convert multiplicative relationships into additive ones. We present two stable DDA loss functions and augment them with a probability loss, resulting in Probabilistic DDA (PDDA). PDDA effectively minimizes class overlap in output distributions, producing highly confident predictions with reduced within-class variance. When applied to wind blade segmentation, PDDA showcases notable advances in performance and consistency, critical for wind energy maintenance. To our knowledge, this is the first application of DDA to image segmentation.
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