Superpixel-based Road Segmentation for Real-time Systems using CNN Farnoush Zohourian 1 , Borislav Antic 2 , Jan Siegemund 2 , Mirko Meuter 2 and Josef Pauli 1 1 University of Duisburg-Essen, Department of Computer Science and Applied Cognitive Science, 47048, Duisburg, Germany 2 Delphi Electronics & Saftey, D-42119, Wuppertal, Germany Keywords: Superpixel, Semantic Segmentation, CNN, Deep Learning, Road Segmentation. Abstract: Convolutional Neural Networks (CNN) contributed considerable improvements for image segmentation tasks in the field of computer vision. Despite their success, an inherent challenge is the trade-off between accuracy and computational cost. The high computational efforts for large networks operating on the image’s pixel grid makes them ineligible for many real time applications such as various Advanced Driver Assistance Systems (ADAS). In this work, we propose a novel CNN approach, based on the combination of super-pixels and high dimensional feature channels applied for road segmentation. The core idea is to reduce the computational complexity by segmenting the image into homogeneous regions (superpixels) and feed image descriptors extracted from these regions into a CNN rather than working on the pixel grid directly. To enable the necessary convolutional operations on the irregular arranged superpixels, we introduce a lattice projection scheme as part of the superpixel creation method, which composes neighbourhood relations and forces the topology to stay fixed during the segmentation process. Reducing the input to the superpixel domain allows the CNN’s structure to stay small and efficient to compute while keeping the advantage of convolutional layers. The method is generic and can be easily generalized for segmentation tasks other than road segmentation. 1 INTRODUCTION One of the long-lasting goals of computer vision is the automated scene understanding from a variety of ima- ges. Exposing image specification is useful for appli- cations, like image editing, image search and environ- ment perception for autonomous vehicles. Detecting objects like roads, pedestrians, vehicles, traffic signs, etc. is important for many driver-less cars and driver assistance systems. Due to the variability of different factors like colour, shape, illumination and shadows or obstacles on the road surface, the road detection is a challenging problem. The state of arts techniques to solve this problem are mainly based on deep learning and Convolutional Neural Networks (CNNs) (LeCun et al., 2015; Schmidhuber, 2015). These methods ena- ble towards better visual understanding by applying a semantic segmentation process in which each pixel is assigned to an object category. The segmentation re- sult provides meaningful information to support hig- her level scene understanding tasks. Currently, there are two major approaches to train CNN-based image processing systems. The two ap- proaches differ with respect to the input data mo- del. One of the approaches is based on a patch-wise analysis of the images, i.e. an extraction and clas- sification of rectangular regions having a fixed size for every single image (Ciresan et al., 2012; Farabet et al., 2013; Ganin and Lempitsky, 2014; Ning et al., 2005). The other one is based on full image reso- lution, wherein all pixels of an image in the original size are analyzed (Long et al., 2015). Most recent improvements in both CNN-based methods were ac- complished by increasing the network size (Simonyan and Zisserman, 2014; He et al., 2016), whereas dee- per networks provoke large computational costs that make them unsuitable for embedded devices in driver assistance systems. In the current work we apply a superpixel-based CNN method for the specific application of pixel-wise road segmentation that uses superpixels as input data model. To the best of our knowledge, it is the first time that irregular superpixels with regular lattice pro- jection for Convolutional purpose is given as input data model into a CNN network. The proposed met- hod comprises the following steps: first, segmenting the image into superpixels, wherein the superpixels are coherent image regions comprising a plurality of Zohourian, F., Antic, B., Siegemund, J., Meuter, M. and Pauli, J. Superpixel-based Road Segmentation for Real-time Systems using CNN. DOI: 10.5220/0006612002570265 In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP, pages 257-265 ISBN: 978-989-758-290-5 Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved 257