Curvilinear Structure Segmentation Dataset

This dataset comprises images of curvilinear structures captured across eight distinct natural scene categories: branch, crack, floor, scratch, soil, wire, leaf, and tyre. It serves as a comprehensive benchmark for curvilinear structure segmentation, offering diverse visual patterns with variations in texture, lighting, and structural complexity. The dataset is designed to challenge and advance segmentation models by providing real-world curvilinear structures with irregular shapes, varying widths, and complex backgrounds. By covering a wide range of natural scenes, it supports the development and evaluation of both deep learning-based and traditional segmentation methods. This resource facilitates research on improving the accuracy and robustness of curvilinear structure segmentation algorithms.