| Summary: | Most of existing semantic segmentation models apply receptive fields of a single size in each convolution layer,which deters the models from extracting multi-scale features.To address the problem,this paper implements selective kernel convolution to build a novel residual module,Selective-Kernel-Array-Shuffle(SKAS).The selective kernel convolution can obtain the multi-scale information by adjusting the size of the receptive field.Also,a layer-wise grouped convolution method is proposed to build a lightweight network structure,SKASNet.The number of groups vary in continuous SKAS blocks in order to reduce the number of network parameters in a relatively smooth way and enhance the exchanges of information between different groups.Experimental results on the Cityscapes dataset show that the proposed network model has only 1.7 M parameters,and the segmentation accuracy reaches 68.5%.Compared with SegNet,ICNet,PSPNet and other models,the proposed model can achieve excellent segmentation performance while the number of network parameters is greatly reduced.
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