X-Enhanced ULite: Improving semantic segmentation for surface defects

Quwei Rao

doi:10.1016/j.dsp.2025.105635

X-Enhanced ULite: Improving semantic segmentation for surface defects

2025-10-09

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DOI：10.1016/j.dsp.2025.105635

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In surface defect segmentation, achieving high accuracy while maintaining lightweight model characteristics is crucial for industrial applications. However, designing lightweight models that achieve high accuracy without compromising computational efficiency remains a significant challenge. In this work, we propose X-ULite, an enhanced version of the ULite framework, designed to improve both accuracy and efficiency in defect segmentation. The core innovation lies in the XConv module, which decouples main and anti-diagonal convolutions into independent depthwise branches, thereby preserving orientation-specific features that are crucial for representing cracks and scratches. Additionally, a new BottleNeck module integrates XConv with Axial Depthwise Convolution (AxialDW) and standard depthwise convolution, jointly modeling axial, diagonal, and local features to achieve a comprehensive perception of defect regions. Evaluated on industrial datasets, XULite achieves 85.98 % mIoU on steel surfaces (NEU-Seg), 75.07 % mIoU on Magnetic Tile Defect datasets, and 91.44 % mIoU on Mobile phone screen surface defect (MSD) datasets with only 0.97M parameters. The model maintains low computational complexity and parameters while demonstrating robust segmentation accuracy across diverse industrial scenarios.

X-Enhanced ULite: Improving semantic segmentation for surface defects

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