CLRKDNet: Speeding up Lane Detection with Knowledge Distillation

2024-05-21 · Weiqing Qi, Guoyang Zhao, Fulong Ma, Linwei Zheng, Ming Liu

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.

📄 PDF Abstract BibTeX arXiv:2405.12503

Code (1)

weiqingq/CLRKDNet 공식 구현 pytorch

Tasks

Autonomous Driving Knowledge Distillation Lane Detection

Methods 이 논문이 사용한 방법론

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FPN 설명 없음

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