Abstract:

 A method of implementing lane region segmentation was proposed by using fully convolutional network (FCN) for lightweight and real-time detection of intelligent vehicles with a high accuracy and robustness of lane recognition. A symmetry-structured fully convolutional network was used to predict the lane area pixel by pixel: the convolution and pooling were used to extract the lane features, and the up sampling wes aided by pooling indices, and convolution were used to recover the feature information. Under the established network structure, the effects of convolution kernels of 3×3, 5×5 and 7×7 sizes on the performance of the model were compared. The FCN with skip layers and the FCN without skip layers were compared with the proposed network based on FCN-32s and FCN-16s. The results show that the proposed algorithm is accurate, robust and accurate in real-time processing, and the segmentation is better than traditional FCNs. The small convolution kernel (3×3) method has the best real-time handling speed of 53 frames per second among the three different sizes. Therefore, the proposed algorithm is suitable for road perception for autonomous driving.

Key words: intelligent vehicles  , lane detection , real-time detection , lane region segmentation , deep learning , fully convolutional network (FCN) ,  size of convolution kernel