Road lanes are integral components of the visual perception systems in
intelligent vehicles, playing a pivotal role in safe navigation. In lane
detection tasks, balancing accuracy with real-time performance is essential,
yet existing methods often sacrifice one for the other. To address this
trade-off, we introduce CLRKDNet, a streamlined model that balances detection
accuracy with real-time performance. The state-of-the-art model CLRNet has
demonstrated exceptional performance across various datasets, yet its
computational overhead is substantial due to its Feature Pyramid Network (FPN)
and muti-layer detection head architecture. Our method simplifies both the FPN
structure and detection heads, redesigning them to incorporate a novel
teacher-student distillation process alongside a newly introduced series of
distillation losses. This combination reduces inference time by up to 60% while
maintaining detection accuracy comparable to CLRNet. This strategic balance of
accuracy and speed makes CLRKDNet a viable solution for real-time lane
detection tasks in autonomous driving applications.

为了解决在自动驾驶应用中的实时车道检测任务中精确度和速度之间的平衡问题，我们介绍了 CLRKDNet，这是一个能够在保持检测精度的同时提供实时性能的简化模型。通过简化模型结构并引入新的蒸馏方法和蒸馏损失函数，我们实现了多达 60% 的推理时间减少以及与 CLRNet 相媲美的检测精度，从而在自动驾驶应用的实时车道检测任务中提供了一种切实可行的解决方案。