Three-Skips CNN for road scene semantic segmentation

In this paper we propose a deep learning architecture to make the best use of global and local information for pixel-wise semantic segmentation. The architecture of three-skips CNN is built with convolutional layers in VGG16 network and its mirrored convolutional layers. Our architecture aims to road scene understanding. In order to save memory and computational time, we use unpooling layers to map low resolution feature maps to the input resolution. We introduce three skip architectures which combine local information and global information to produce accurate and detailed segmentations. Besides, we present the median balance method to deal with class unbalance problem in road scene datasets. Thorough evaluations on CamVid dataset demonstrate our approach has state-of-the-art performance and less computational time.