MLP-Based Efficient Convolutional Neural Network for Lane Detection

Lane detection is an important and fundamental task in autonomous driving. Modern convolutional neural network (CNN) methods have achieved high performance in lane detection; however, the intrinsic locality of convolution operations makes these methods limited in effectively modeling the long-range dependencies that are vital to capture global information of lanes. Additionally, numerous convolution operations result in considerable computational cost for high complexity. To overcome these difficulties, we propose an efficient lane detection method by combining CNN with a multilayer perceptron (MLP). First, an improved bottleneck-1D layer is used to replace the standard convolutional layer in overall network to reduce the computational cost and parameters while applying hybrid dilated convolution (HDC) to better capture multiscale lane information. Second, we construct a hybrid MLP block in the latent space to capture the long-range dependencies of lanes. The hybrid MLP projects tokenized convolutional features from spatial locations and channels, and then, they are fused together to obtain global representation, in which each output pixel is related to each input pixel. The introduction of MLP further decreases computational complexity and makes the proposed architecture more efficient for lane detection. Experimental results on two challenging datasets (CULane, Tusimple) demonstrate that our method can achieve a higher computational efficiency while maintaining a decent detection performance compared with other state-of-the-art methods. Furthermore, this study indicates that integrating the global representation capacity of an MLP with local prior information of convolution is an effective and potential perspective in lane detection.