Street Scene understanding via Semantic Segmentation Using Deep Learning

Abstract

Scene classification is an essential conception task used by robotics for understanding the environment. Like the street scene, the outdoor scene is composed of images with depth that has a greater variety than iconic object images. Image semantic segmentation is an important task for Autonomous driving and Mobile robotics applications because it introduces enormous information needed for safe navigation and complex reasoning. This paper provides a model for semantic segmentation of outdoor sense to classify each object in the scene. The proposed network model generates a hybrid model that combines U-NET with Xception networks to work on 2.5 dimensions cityscape dataset, which is used for 3D applications. This process contains two stages. The first is the pre-processing operation on the RGB-D dataset (data Augmentation and k- means cluster). The second stage designed the hybrid model, which achieves a pixel accuracy is 0.7874. The output module is generated using a computer with GPU memory NVIDIA GeForce RTX 2060 6G, programming with python 3.7.