Lightweight networks have become a key technology for industrial scenarios.On the purpose to improve the feature representation ability and reduce the number of parameters of the Ghost module,we proposed an improved S-Ghost Bottleneck module.The 1×1 convolution channel is introduced in parallel with the original Ghost module.The number of channels in the Ghost module is reduces to compress the parameter scale.The paralleled 1×1 convolution channel is on the purpose to expand the number of channels.The channel shuffle operation is adopted at the output of the proposed module to enhance the channel interaction.Experimental results show that,the image classification network LGSNet(Light Ghost Networks,LGSNet)composed with the proposed structure has significantly reduced the computational complexity and the scale of the parameters.The accuracy of the proposed network has no significant change,and even achieves the best in some tests.The result shows that the proposed method is a promising solution for lightweight network design for industrial scenarios.
维普
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