Research of Multi-Scale Perceptual Fusion Network for Urban Scene Segmentation
In order to solve the problem of multi-scale transformation of road scene and adapt to the requirements of automatic driving semantic scene,and reduce the complexity of the whole structure of convolutional neural network model,this paper propos-es a multi-scale perceptual fusion semantic segmentation network based on asymmetric network structure of decoder to segment road image.According to the idea of residual network and space convolution,a new Res-SS residual module is designed to improve the efficiency of feature acquisition.The multi-scale perceptual fusion extraction module is designed and adopted to extract more multi-scale feature information from different receptive fields for weighted fusion,so as to improve the robustness of the network.Be-cause the edge information of the segmented object is lost in the process of feature extraction,a Superpixel segmentation module is used to fuse the low-level information with the high-level information,so as to recover the lost information of the feature map.Exper-iments on Cityscapes dataset show that the algorithm has higher accuracy and robustness than the existing semantic segmentation al-gorithms.
semantic segmentationconvolutional neural network(CNN)residual modulemulti-level featuresfeature fu-sionedge information
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