A lightweight bumblebee image classification model based on improved GhostNet V2
In order to realize automatic sorting of bumblebees accurately and quickly,a lightweight deep learning bumblebee image classification model is proposed.Firstly,1 742 images of queen,drone and worker bees of ground bumblebee were collected,and a dataset containing 13 117 bumblebee images was constructed by data augmentation.Then,based on the GhostNet V2 model,the feature information of the input image under more receptive fields was obtained by multi-scale convolution,two shortcut branches were added to fuse the features of the low-level and the middle-level,the high-level respectively,the ReLU activation function was replaced by SiLU,and the number of bottleneck layers and channels was cut,and a lightweight bumblebee image classification model GMCFF was de-signed.The results showed that the classification accuracy of the BumblebeeImage dataset using the GMCFF model reached 98.40％,which was 1.53 percentage points higher than the original model,and the classification accuracy compared with the ShuffleNetV2 and MobileNetV2 model was also higher,increasing by 1.53 and 1.15 percentage points respectively.The number of parameters of the model was only 0.73 M,the amount of floating point operations was decreased by 25.15 M compared with that before the improvement,the model size was only 3.01 MB,and the average test time of a single bumblebee image was 17.08 ms,which met the requirements of lightweight and real-time.
NETL
NSTL
万方数据
