Discriminant Learning-based Colorspace for Blade Segmentation

TL;DR

This paper introduces a novel deep learning-based multidimensional discriminant analysis method, CSDA, that optimizes color spaces for improved blade segmentation accuracy in wind turbine images.

Contribution

It extends Linear Discriminant Analysis into a deep learning framework, enabling end-to-end training for tailored color representation in segmentation tasks.

Findings

Significant accuracy improvements on wind turbine blade data

Effective end-to-end optimization of colorspace and segmentation

Demonstrates importance of tailored preprocessing in domain-specific segmentation

Abstract

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.