Semantic Segmentation Model for Road Scene Based on EncoderDecoder Structure Semantic segmentation as a pixelwise segmentation task provides rich object information, which is an important research topic in robotic perception. It has been widely applied in many fields, such as autonomous driving and robot navigation. In the application of understanding road scene, the semantic segmentation model should accurately describe the appearance and shape of different categories of objects. In addition, the semantic segmentation model need to understand the spatial relationships between different categories. In order to improve the performance of semantic segmentation model for road scene, we present a model based on encoderdecoder structure with dilated convolution. We apply this model on the Cityscapes dataset and compare it with other classical models. To assess performance, we rely on the standard Jaccard Index IoU (Intersection over Union) and mIoU (mean Intersection over Union). The experimental results verify that this model can effectively improve the performance of semantic segmentation and meet the requirements for road scene.

Preserving details in semanticsaware context for scene parsing Great success of scene parsing (also known as, semantic segmentation) has been achieved with the pipeline of fully convolutional networks (FCNs). Nevertheless, there are a lot of segmentation failures caused by large similarities between local appearances. To alleviate the problem, most of existing methods attempt to improve the global view of FCNs by introducing different contextual modules. Though the reconstructed high resolution output of these methods is of rich semantics, it cannot faithfully recover the fine image details owing to lack of desired precise lowlevel information. To overcome the problem, we propose to improve the spatial decoding process through embedding possibly lost lowlevel information in a principled way. To this end, we make the following three contributions. First, we propose a semantics conformity module to make lowlevel features variations agnostic. Second, we introduce semantics into the conformed low level features through guidance from semantically aware features. Finally, we institute the availability of various possible contextual features at feature fusion to enrich context information. The proposed approach demonstrates competitive performance on challenging PASCAL VOC 2012, Cityscapes, and ADE20K benchmarks in comparison to the stateoftheart methods.