Widening residual refine edge reserved neural network for semantic segmentation

Abstract

Over the past several years, deep convolutional neural networks have pushed the performance of computer vision systems to soaring heights on semantic segmentation. In this paper, we put forward a novel practical convolutional neural network for effective semantic segmentation. The network is comprised of three modules. The module of widening residual convolutional neural network is firstly used to generate a full-size segmentation with localization of object boundaries. Feature maps generated by widening residual network are integrated by the module called as refine residual feature pyramid. The module of rolling guidance edge reserved layer is used for improving segmentation boundary. The innovations of this paper consist of these points. Widening residual skipped connections are utilized to preserve low-level features allowing accurate boundary localization. The refine residual feature pyramid is incorporated to achieve the observation of existence of objects at multiple scale. For modeling long-range context dependence and increasing accuracy near object boundary, rolling guidance edge reserved layer are implemented. The experimental results, on PASCAL VOC 2012 semantic segmentation dataset and Cityscapes dataset, demonstrate the proposed method can offer an effective way of producing pixel-wise image labeling.