Real-time lane detection in embedded systems encounters significant challenges due to subtle and sparse visual signals in RGB images, often constrained by limited computational resources and power consumption. Although deep learning models for lane detection categorized into segmentation-based, anchor-based, and curve-based methods there remains a scarcity of universally applicable optimization techniques tailored for low-power embedded environments. To overcome this, we propose an innovative Covariance Distribution Optimization (CDO) module specifically designed for efficient, real-time applications. The CDO module aligns lane feature distributions closely with ground-truth labels, significantly enhancing detection accuracy without increasing computational complexity. Evaluations were conducted on six diverse models across all three method categories, including two optimized for real-time applications and four state-of-the-art (SOTA) models, tested comprehensively on three major datasets: CULane, TuSimple, and LLAMAS. Experimental results demonstrate accuracy improvements ranging from 0.01% to 1.5%. The proposed CDO module is characterized by ease of integration into existing systems without structural modifications and utilizes existing model parameters to facilitate ongoing training, thus offering substantial benefits in performance, power efficiency, and operational flexibility in embedded systems.

翻译：嵌入式系统中的实时车道线检测面临显著挑战，这主要源于RGB图像中细微且稀疏的视觉信号，并常受限于有限的计算资源和功耗。尽管基于分割、基于锚点和基于曲线的深度学习方法构成了车道线检测模型的主要类别，但目前仍缺乏专门为低功耗嵌入式环境设计的、具有普适性的优化技术。为克服此问题，我们提出了一种创新的协方差分布优化模块，专为高效实时应用而设计。该模块使车道线特征分布与真实标注紧密对齐，在未增加计算复杂度的前提下显著提升了检测精度。评估在涵盖所有三种方法类别的六个不同模型上进行，其中包括两个为实时应用优化的模型和四个最先进的模型，并在CULane、TuSimple和LLAMAS三个主要数据集上进行了全面测试。实验结果表明，准确率提升范围在0.01%至1.5%之间。所提出的CDO模块具有易于集成到现有系统而无需结构修改的特点，并利用现有模型参数以促进持续训练，从而在嵌入式系统的性能、能效和操作灵活性方面提供了显著优势。