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<p>Semantic segmentation, a fundamental task in computer vision, aims to label each pixel in an image with a semantic category. Despite advancements, balancing segmentation accuracy and real-time inference speed remains challenging, particularly for lightweight networks. This paper proposes MSDSeg, a lightweight real-time semantic segmentation network employing multi-scale dilated convolutions. The encoder incorporates a Multi-scale Dilation Block (MSDB) featuring three varying dilated convolutions with distinct dilation rates, achieving good results without pre-training on large datasets. The decoder introduces a Cross-layer Attention Fusion Module (CAFM) to efficiently merge multi-level feature information, reducing the disparity between high-level and low-level features. Additionally, a Feature Enhancement Head (FEH) utilizing global average and maximum pooling is employed to improve object and boundary detection. Extensive experiments on the Cityscapes and CamVid datasets demonstrate that MSDSeg achieves a balance between accuracy and speed, with segmentation accuracies of 74.0% and 75.3% mIoU, and inference speeds of 204.7 and 175.0 FPS, respectively. Here, we show that MSDSeg effectively addresses the trade-off between accuracy and efficiency in real-time semantic segmentation. The code of this work is publicly available at: <a href="https://github.com/wangyunlei-wyl/MSDSeg.">https://github.com/wangyunlei-wyl/MSDSeg.</a></p>

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Lightweight and real-time semantic segmentation network via multi-scale dilated convolutions

  • 期刊论文

  • 发表日期：

    2025年8月18日

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  • Shan Zhao,
  • Yunlei Wang,
  • Zhanqiang Huo,
  • Fukai Zhang

摘要

Semantic segmentation, a fundamental task in computer vision, aims to label each pixel in an image with a semantic category. Despite advancements, balancing segmentation accuracy and real-time inference speed remains challenging, particularly for lightweight networks. This paper proposes MSDSeg, a lightweight real-time semantic segmentation network employing multi-scale dilated convolutions. The encoder incorporates a Multi-scale Dilation Block (MSDB) featuring three varying dilated convolutions with distinct dilation rates, achieving good results without pre-training on large datasets. The decoder introduces a Cross-layer Attention Fusion Module (CAFM) to efficiently merge multi-level feature information, reducing the disparity between high-level and low-level features. Additionally, a Feature Enhancement Head (FEH) utilizing global average and maximum pooling is employed to improve object and boundary detection. Extensive experiments on the Cityscapes and CamVid datasets demonstrate that MSDSeg achieves a balance between accuracy and speed, with segmentation accuracies of 74.0% and 75.3% mIoU, and inference speeds of 204.7 and 175.0 FPS, respectively. Here, we show that MSDSeg effectively addresses the trade-off between accuracy and efficiency in real-time semantic segmentation. The code of this work is publicly available at: https://github.com/wangyunlei-wyl/MSDSeg.