Lane detection plays a pivotal role in the field of autonomous vehicles and advanced driving assistant systems (ADAS). Over the years, numerous algorithms have emerged, spanning from rudimentary image processing techniques to sophisticated deep neural networks. The performance of deep learning-based models is highly dependent on the quality of their training data. Consequently, these models often experience a decline in performance when confronted with challenging scenarios such as extreme lighting conditions, partially visible lane markings, and sparse lane markings like Botts' dots. To address this, we present an end-to-end lane detection and classification system based on deep learning methodologies. In our study, we introduce a unique dataset meticulously curated to encompass scenarios that pose significant challenges for state-of-the-art (SOTA) models. Through fine-tuning selected models, we aim to achieve enhanced localization accuracy. Moreover, we propose a CNN-based classification branch, seamlessly integrated with the detector, facilitating the identification of distinct lane types. This architecture enables informed lane-changing decisions and empowers more resilient ADAS capabilities. We also investigate the effect of using mixed precision training and testing on different models and batch sizes. Experimental evaluations conducted on the widely-used TuSimple dataset, Caltech lane dataset, and our LVLane dataset demonstrate the effectiveness of our model in accurately detecting and classifying lanes amidst challenging scenarios. Our method achieves state-of-the-art classification results on the TuSimple dataset. The code of the work will be published upon the acceptance of the paper.

翻译：车道检测在自动驾驶车辆和高级驾驶辅助系统（ADAS）领域发挥着关键作用。多年来，从基础图像处理技术到复杂的深度神经网络，各类算法层出不穷。基于深度学习模型的性能高度依赖于训练数据的质量，因此，在面对极致光照条件、部分可见的车道标线以及稀疏车道标线（如Botts'点）等挑战性场景时，这些模型性能往往下降。为解决这一问题，我们提出了一种基于深度学习的端到端车道检测与分类系统。研究中引入了一个精心整理的独特数据集，该数据集涵盖了当前最先进（SOTA）模型难以处理的场景。通过微调选定的模型，我们旨在提升定位精度。此外，我们提出一个基于CNN的分类分支，与检测器无缝集成，实现对不同车道类型的识别。此架构支持智能换道决策，并增强ADAS的鲁棒性。我们还研究了在不同模型和批次大小下使用混合精度训练和测试的影响。在广泛使用的TuSimple数据集、Caltech车道数据集及我们构建的LVLane数据集上的实验评估表明，我们的模型能在挑战性场景中准确检测和分类车道。我们的方法在TuSimple数据集上达到了分类任务的SOTA结果。本文代码将在论文接收后公开。