A Real-Time Semantic Segmentation Approach for Autonomous Driving Scenes

An important part of autonomous driving is the perception of the driving environment of the car,which has created a strong demand for high precision semantic segmentation algorithms that can be run in real time on low-power mobile devices.However,when analyzing the factors that affect the accuracy and speed of the semantic segmentation network,it can be found that in the structure of the previous semantic segmentation algorithm,spatial information and context features are difficult to take into account at the same time,and using two networks to obtain spatial information and context information separately will increase the amount of calculation and storage.Therefore,a new structure is proposed that divides the spatial path and context path from the network based on the residual structure,and a two-path real-time semantic segmentation network is designed based on this structure.The network contains a feature fusion module and an attention refinement module,which are used to realize the function of fusing the multi-scale features of two paths and optimizing the output results of context path.The network is based on the PyTorch framework and uses NVIDIA 1080 Ti graphics cards for experiments.On the road scene data set Cityscapes,mIoU reached 78.8%,and the running speed reached 27.5 fps.