Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction

Abstract

The application of deep learning in the field of image semantic segmentation has greatly improved the accuracy of segmentation,but due to the limitations of speed and memory,these models can not be directly applied to embedded devices for real-time segmentation.Aiming at the problems of complex network structure and huge computation cost of semantic segmentation model,a real-time semantic segmentation algorithm based on multi-path feature extraction combined with edge detection algorithm is proposed.The model uses Sobel operator,Scharr operator and Laplacian operator to extract the contour information of the image.The algorithm designs the spatial path to extract the spatial position information of the image,designs the semantic path to extract the advanced semantic information of the image,and uses the edge detection path to extract the representative texture features of the image.The ghost lightweight module is used to reduce the amount of model parameters and improve the segmentation speed of the algorithm.Experimental results on 480 pixel and 360 pixel CamVid dataset show that the segmentation accuracy of the model can be improved on the three edge detection operators,especially when the Sobel operator with the size of 3×3 is added,the performance of the algorithm is improved most obviously,and the segmentation accuracy reaches 42.9% on the basis of the image processing speed of 349 frames/s on CamVid test set.Both the segmentation accuracy and segmentation speed achieve good results,and achieve a good balance between real-time and accuracy.

Keywords

• deep learning|edge detection|semantic segmentation in real-time|feature fusion|multi-feature extraction