关键词: 深度学习, 卷积神经网络, 场景理解, 语义分割

Abstract: In the self-driving technology, the road scene understanding is a very important task for environment perception, and it is a challenging topic. In this paper, a deep Road Scene Segmentation Network（RSSNet） is presented, which is a 32-layer full convolutional network composed of convolution encoded network and deconvolution decoded network. The batch normalization layer used in the RSSNet prevents the vanishing gradient problem from appearing during the training process; the activation layer using the Maxout function further weakens the vanishing gradient and avoids the network falling into a saturated mode and neuron death phenomenon; moreover, the RSSNet using dropout operation prevents the over-fitting phenomenon of the network model; the max-pool indices of the feature map saved by the encoded-network are used in the decoded-network to upsample the feature map, which keeps the important edge information down. The experimental results show that the RSSNet can greatly improve the training efficiency and the segmentation accuracy, effectively classify each pixel in the road scene image and smoothly segment the objects, and provide useful information of road environment for driverless cars.

Key words: deep learning, convolutional neural network, scenes understanding, semantic segmentation