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<p>To address the core challenge faced by existing lightweight scene parsing networks—balancing multiscale feature representation precision and computational efficiency (rather than “difficulties in extracting multi-scale information”)—this paper proposes MSPNet, a lightweight multiscale scene parsing network. The network adopts StarNet as the backbone to leverage its efficient low-to-high dimensional feature transformation capability, and innovatively embeds the Efficient Pixel Localization Attention (EPLA) module into the PSPNet architecture. Unlike simple module stacking, the EPLA module integrates two synergistic submodules: ELA (Efficient Localization Attention) and PagFM (Pyramid Attention-Guided Feature Module). The ELA module uses a dynamic weight allocation mechanism to achieve precise pixel-level feature localization while reducing attention computation overhead by 38%; the PagFM module constructs a hierarchical pyramid fusion architecture, adaptively guiding cross-scale feature integration to enhance small-target representation. Additionally, MSPNet incorporates depthwise separable convolutions and channel reparameterization techniques, further optimizing model compactness. Experimental results on the Pascal VOC2012 validation set show that MSPNet achieves a mean Intersection over Union (mIoU) of 87.19%, a 1.79% improvement over PSPNet. With GFLOPs (9.7G for StarNet-s4 backbone) and parameter counts (7.4&#xa0;M) comparable to the MobileNet series, MSPNet outperforms contemporary lightweight SOTA models in both accuracy and efficiency, providing an effective solution for real-time semantic segmentation on resource-constrained mobile devices. The code for MSPNet is available at <a href="https://github.com/Eric-863/MSPnet">https://github.com/Eric-863/MSPnet</a>.</p>

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Multiscale scene parsing network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月2日

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  • YuanYuan Wang,
  • Zining Zhao,
  • Yilin Liu,
  • Jibin Wang,
  • Haiyan Zhang,
  • Jiajun Wang,
  • Luyue Liu

摘要

To address the core challenge faced by existing lightweight scene parsing networks—balancing multiscale feature representation precision and computational efficiency (rather than “difficulties in extracting multi-scale information”)—this paper proposes MSPNet, a lightweight multiscale scene parsing network. The network adopts StarNet as the backbone to leverage its efficient low-to-high dimensional feature transformation capability, and innovatively embeds the Efficient Pixel Localization Attention (EPLA) module into the PSPNet architecture. Unlike simple module stacking, the EPLA module integrates two synergistic submodules: ELA (Efficient Localization Attention) and PagFM (Pyramid Attention-Guided Feature Module). The ELA module uses a dynamic weight allocation mechanism to achieve precise pixel-level feature localization while reducing attention computation overhead by 38%; the PagFM module constructs a hierarchical pyramid fusion architecture, adaptively guiding cross-scale feature integration to enhance small-target representation. Additionally, MSPNet incorporates depthwise separable convolutions and channel reparameterization techniques, further optimizing model compactness. Experimental results on the Pascal VOC2012 validation set show that MSPNet achieves a mean Intersection over Union (mIoU) of 87.19%, a 1.79% improvement over PSPNet. With GFLOPs (9.7G for StarNet-s4 backbone) and parameter counts (7.4 M) comparable to the MobileNet series, MSPNet outperforms contemporary lightweight SOTA models in both accuracy and efficiency, providing an effective solution for real-time semantic segmentation on resource-constrained mobile devices. The code for MSPNet is available at https://github.com/Eric-863/MSPnet.