A lightweight convolutional neural network for road surface classification under shadow interference

Abstract

The development of intelligent driving, especially in the intelligent control of active suspension, heavily relies on the predictive perception of upcoming road conditions. To achieve accurate real-time road surface classification and overcome shadow interference, a lightweight convolutional neural network (CNN) based on a novel data augmentation method is proposed and an improved cycle-consistent adversarial network (CycleGAN) is developed to generate shadowed pavement data. The CycleGAN network structure is optimized using the texture self-supervised (TSS) mechanism and the learned perceptual image patch similarity (LPIPS) function, with label smoothing applied during training. The images produced by this data augmentation method closely resemble real-world images. Furthermore, Efficient-MBConv, which offers the advantages of fewer parameters and higher precision, is proposed. Finally, the Light-EfficientNet architecture, based on Efficient-MBConv, is developed and trained on the augmented dataset. Compared with EfficientNet-B0, the number of parameters in Light-EfficientNet is reduced by 61.94 %. The Light-EfficientNet model trained with data augmentation demonstrates an average classification accuracy improvement of 5.76 % on the test set with shadows, compared with the model trained without data augmentation. This approach effectively reduces the impact of shadows on road classification at a lower cost, while also significantly reducing the computational resources required by the CNN, providing real-time and accurate road surface information for the control of active suspension height and damping.