S-MobileNetV2+SegNet Model and Rapid Identification of Sugarcane

At present, there are many deep learning models in the identification field, but they usually have problems with identification accuracy and slow speed. In this paper, we propose the S-MobileNetV2+SegNet model to identify sugarcane, and improve the SegNet model by replacing the encoder VGG16 model with the reduced structure Mobile-NetV2 model and introducing different ratios of dilated convolution to expand the local receptive field to eliminate the problem of insufficient information capture. Then reduce the network structure of the decoder and the number of convolution kernels to achieve a network with fewer parameters. To verify the accuracy and speed of the S-MobileNetV2+SegNet model in sugarcane identification, it is compared with SegNet, DeepLabV3+, and DeepLab-V3+Mobile-NetV2 models. The experimental results show that the S-MobileNetV2+SegNet model has better results and performance for sugarcane identification.