SF-CrackNet: A Spatial-Frequency Dual-Branch Network for Pavement Crack Segmentation
编号:13 访问权限:仅限参会人 更新:2026-09-11 21:48:19 浏览:12次 张贴报告

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摘要
Pavement crack segmentation remains challenging because cracks are typically thin, low-contrast, spatially discontinuous, and easily confused with shadows, stains, and complex pavement textures. To address these issues, this paper proposes SF-CrackNet, a spatial-frequency dual-branch network that combines a spatial detail encoder (SDE) with a frequency-aware Transformer (FAT). The SDE employs multidirectional convolutional modeling to preserve local crack edges, directional patterns, and elongated structures, while the FAT captures long-range contextual dependencies and frequency-aware representations. Multiscale features from the two branches are aligned at corresponding stages, fused by parameter-free element-wise addition, and progressively decoded to recover pixel-level crack regions. Experiments are conducted on the Crack500 and Crack300 datasets under a unified training-from-scratch protocol. SF-CrackNet achieves an F1-score of 80.26% and an IoU of 67.03% on Crack500, together with a Recall of 83.91% on Crack300. These results indicate that the proposed framework provides competitive segmentation performance while maintaining effective crack-region recall across different pavement scenes.
关键词
Semantic segmentation; pavement crack segmentation; dual-branch network; Transformer; frequency-aware modeling; additive fusion
报告人
Xinyi Liu
Student Soochow University

稿件作者
Xinyi Liu Soochow University
Zheyu Hua Soochow University
Zheng Xu Soochow University
MingXuan Yan Soochow University
xiaojun Zhang Soochow University
Zhi Tao Soochow University
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重要日期
  • 会议日期

    11月06日

    2026

    11月08日

    2026

  • 10月15日 2026

    初稿截稿日期

主办单位
IEEE Instrumentation and Measurement Society
承办单位
Sichuan University
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