Stacking CNN-Based Deep Networks for Lane Detection on CULane: A performance-driven approach

Robust Lane detection is critical for autonomous driving and high-end driver-assistance systems (ADAS) but remains challenging in adverse conditions. This research proposes a lane segmentation technique based on deep learning models from the CULane dataset with the aid of U-Net, ResNet, VGG, Xception and DenseNet architectures. For the sake of robustness and generalization, a stacking ensemble approach is proposed to integrate predictions from an ensemble of models. The stack ensemble method within this suggested approach effectively incorporates the merits of individual architectures to improve the accuracy of lane segmentation, particularly in challenging situations such as shadows and low light. Our experimental results indicate that the ensemble strategy outperforms single models in terms of accuracy, dice coefficient, and mIoU. Our findings show that the ensemble model improves segmentation accuracy by 87% over the best individual CNN model, making it a promising approach for real-world autonomous driving applications.