Lightweight Learning Algorithm for Lane Line Detection in Different Lighting Conditions

Lane line detection is a critical task in autonomous driving and autonomous vehicle navigation that requires fast and accurate prediction. For the lane detection task, an improved lightweight lane detection method is proposed by combining multi-scale feature fusion technology and attention mechanism to improve the accuracy and efficiency. Firstly, the original ResNet lane feature extraction algorithm is replaced with RepVGG algorithm, and to further utilize low-level features, a scheme of multi-scale lane feature extraction is designed. Then, to increase the capture ability on the lane regions and expand the network's receptive field, a lane dual attention (LDA) module dedicated to lane feature extraction is proposed. Finally, to address the problem of imbalance in lane samples, focal loss function is selected to replace the cross entropy loss function. Experimental results on the CULane test dataset show the overall superior performance on detection accuracy and speed in different lighting and traffic environments.