Research on Insect Identification Model Algorithm for Agricultural Lamp-baiting Images
[Purposes]This study aims to identify 28 types of farmland pests with small samples and un-balanced datasets using Faster R-CNN algorithm.[Methods]Firstly,the impact of different input image sizes on the performance of the training model is analyzed,and the optimal selection is determined as 25％ of the input image size.Secondly,in order to avoid overfitting problems caused by too few pest data for some classifications,Mixup and Mosaic methods are used to increase data diversity,and transfer learning is used to improve the model performance.[Findings]These methods can effectively improve the generalization ability and robustness of the model.Except for the two kinds of pests No.9 and No.10,which have very high similarity and low AP value,the average AP value of other pests reaches 92.07％.[Conclusions]The generalization ability of the model is verified by the test data.The model performs well but still has room for improvement.
pest identificationdata augmentationFaster R-CNNdeep learningobject detection
NETL
NSTL
万方数据
