In order to reduce the deployment cost of deep convolutional neural networks and optimize the detection performance of the models,an improved lightweight backbone network algorithm S-GhostNet is proposed.The algorithm reduces the computational effort of convolutional operations by introducing a Ghost Module structure optimized for feature map generation.It improves the accuracy of the models by combining with an improved class of residual modules.S-GhostNet has a strong plug-and-play property and can be applied to most convolutional neural network models.Experimental results show that S-GhostNet is less computationally intensive than MobileNetV2,ShuffleNetV2 and GhostNet in target classification and target detection tasks.Also the accuracy of the model is similar or even higher.
维普
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