Implementation of Small Sample Citrus Fruit Classification Detection Based on Improved ResNet18 Residual Network

Citrus is an important means of production, and traditional citrus classification using manual labor is time-consuming, labor-intensive, and costly. This article proposes a new residual block structure based on the introduction of pre activated residual block mode to build the ResNet18 network model, establish a small sample database of 4000 citrus fruits in four categories and use this network model for classification detection. The average accuracy of the final classification detection of ResNet18 after optimization reached 93.764%, the training time of the model was reduced by 41.47%, and the improved model showed improvements in both training speed and accuracy.