CLRKDNet：通过知识蒸馏加速车道检测
CLRKDNet: Speeding up Lane Detection with Knowledge Distillation
AI总结：
针对现有车道检测方法难以兼顾精度与实时性的问题，提出CLRKDNet，通过简化CLRNet的FPN结构与检测头，结合师生蒸馏及新蒸馏损失，在精度相当的前提下将推理时间减少60%，适用于自动驾驶实时车道检测。
摘要：
道路车道是智能车辆视觉感知系统的核心组成部分，在安全导航中发挥着关键作用。在车道检测任务中，平衡精度与实时性能至关重要，但现有方法往往顾此失彼。为解决这一权衡问题，我们提出了CLRKDNet，这是一款兼顾检测精度与实时性能的轻量化模型。当前最优模型CLRNet在多个数据集上表现出卓越性能，但由于其特征金字塔网络（FPN）和多层检测头架构，计算开销巨大。我们的方法简化了FPN结构与检测头，对二者进行重新设计，融入了新颖的师生蒸馏流程以及一系列新提出的蒸馏损失函数。这种组合将推理时间减少了高达60%，同时保持了与CLRNet相当的检测精度。这种精度与速度的战略性平衡使CLRKDNet成为自动驾驶应用中实时车道检测任务的可行解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
