Two-branch real-time semantic segmentation algorithm based on spatial information guidance
In view of feature spatial information loss caused by the reduction of a large number of parameters in the real-time semantic segmentation model and inaccurate segmentation category prediction caused by the lack of context information of features,a two-branch real-time semantic segmentation algorithm based on spatial information guidance was proposed.In order to better retain the spatial information,the algorithm used a two-branch structure to obtain the spatial and semantic information of features,respectively.A spatial guided module(SGM)was designed to capture the local information and the surrounding context information of the features and give higher weight to the important information through channel weighting,which effectively made up for the image information loss of high-resolution features in the process of downsampling.A pooling feature enhancement module(PFEM)was designed to further enhance the ability of context information characterization of features.Pooling cores of different sizes were used to capture multi-scale feature information,and the long-distance dependence relationship between the features was modeled by strip-shaped pooling cores.The category of the segmentation region was better determined.The proposed algorithm was verified on Cityscapes and CamVid datasets,and the mean intersection over union reached 77.4%and 74.0%,respectively.The detection speed reached 49.1 frames per second and 124.5 frames per second,respectively,which effectively improved the accuracy and achieved good semantic segmentation performance while ensuring real-time segmentation.
imaging processingreal-time semantic segmentationconvolutional neural networksdilated convolutioncontext information
万方数据
维普
