LVLane: Deep Learning for Lane Detection and Classification in Challenging Conditions

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.