A lightweight identification model for alien invasive plants based on improved MobileNet
The primary task in controlling alien invasive plants is to accurately identify the species of invasive plants.However,there are a wide variety of alien invasive plants,and some of them present the inter-class homogeneity or intra-class heterogeneity,which brings challenges to the identification and control of alien invasive plants.In order to identify alien invasive plants accurately,efficiently and in real time,a lightweight identification model based on the improved MoblileNet(MobileNet-LW)was proposed.The 11 628 images of 113 species of alien invasive plants identified by technicians were divided into the training set,verification set and testing set in a ratio of 6∶2∶2.The image data were enhanced by Retinex,rotating image and Gaussion noise.In order to reduce the false detection,the SE channel attention mechanism and deep connection attention network were added to the MobileNet model to improve the ability of key feature extraction.In order to reduce consumption of model computation and memory,the channel pruning method was used to slim down the network.In order to improve the accuracy reduction caused by model pruning,the knowledge distillation of the teacher network-teaching assistant network-student network was adopted to the pruned network,and the student network can improve the recognition accuracy of alien invasive plants through learning the soft knowledge.In this study,the ablation experiments of the model were done.Three indicators including average accuracy,average recall rate and average F1 value were used to evaluate the classical models and the improved model MobileNet-LW.The results of ablation experiments showed that the performance of each improved method on the model was improved on a same testing set.The accuracy of MobileNet-LW increased by 5.4 percentage points in identifying the alien invasive plants,and the number of parameters of model reduced by about 53％.The average accuracies of the five models,i.e.,EfficentNet,DBTNct,ResNet-101,ConvNext and MobileNet-LW,were 72.3％,74.9％,76.1％,79.7％and 86.1％,respectively,showing that the improved model could improve the identification accuracy of alien invasive plants.The alien invasive plant identification model based on the improved MobileNet showed high accuracy in identifying 113 species of alien invasive plants,and showed characteristic of lightweight after pruning.
万方数据
维普
