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<p>To achieve advanced driving functions, intelligent vehicle relies on lane mark captured by an optical sensor in traffic environment. The emerging dynamic vision sensor (DVS) exhibits an impressive dynamic range and time resolution, showing potential as the next generation of on-board sensor to overcome complex lighting conditions like glare, blur, or darkness. Different from the traditional RGB image, the event image produced by the DVS is sparse and lacks color. Although existing methods achieve competitive performance for RGB-based lane mark segmentation, they often result in discontinuous lane mark segmentation when applied to the sparse event image. In this paper, we propose the Event-based Large Kernel Network (EvLKNet) specifically for segmenting sparse event lane marks. The EvLKNet is a lightweight architecture that employs multi-scale large kernel convolution which enhances the ability to capture long-range relationships among sparse event pixels and mitigates the degradation of segmentation accuracy caused by image sparsity. Moreover, intermediate event feature map distillation is used to improve prediction accuracy without increasing inference cost. The performance of EvLKNet surpasses existing advanced methods on two event-based lane mark datasets, DET and Carla-DVS. The lightweight architecture allows EvLKNet to have only 3.83M parameters and a computational overhead of 11.92 GFLOPS.</p>

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Lane mark segmentation from sparse event image via lightweight large kernel network

  • 期刊论文

  • 发表日期：

    2025年12月27日

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  • Mingyong Zhuang,
  • Xinbo Chen,
  • Yixiong Zhang,
  • Jianyang Zhou

摘要

To achieve advanced driving functions, intelligent vehicle relies on lane mark captured by an optical sensor in traffic environment. The emerging dynamic vision sensor (DVS) exhibits an impressive dynamic range and time resolution, showing potential as the next generation of on-board sensor to overcome complex lighting conditions like glare, blur, or darkness. Different from the traditional RGB image, the event image produced by the DVS is sparse and lacks color. Although existing methods achieve competitive performance for RGB-based lane mark segmentation, they often result in discontinuous lane mark segmentation when applied to the sparse event image. In this paper, we propose the Event-based Large Kernel Network (EvLKNet) specifically for segmenting sparse event lane marks. The EvLKNet is a lightweight architecture that employs multi-scale large kernel convolution which enhances the ability to capture long-range relationships among sparse event pixels and mitigates the degradation of segmentation accuracy caused by image sparsity. Moreover, intermediate event feature map distillation is used to improve prediction accuracy without increasing inference cost. The performance of EvLKNet surpasses existing advanced methods on two event-based lane mark datasets, DET and Carla-DVS. The lightweight architecture allows EvLKNet to have only 3.83M parameters and a computational overhead of 11.92 GFLOPS.