基于Gabor特征提取和MobileNetV2在云南天牛识别中的应用OA

Longicorn beetles pose a significant threat to forest health,making their classification and identification crucial for ecology,agriculture,and environmental protection.Typically,existing classification methods rely on traditional morphological taxonomy,which suffers from low efficiency and limited accuracy.This paper focuses on 10 common species of longhorn beetles in Yunnan Province,utilizing a dataset comprising collected specimens and images with natural backgrounds.It applies a 2D Gabor filter to extract image texture features,and introduces the lightweight transfer learning model MobileNetV2 for classification.The study compares the performance of feature extraction methods,including Local Binary Pattern(LBP),Gray Level Co-occurrence Matrix(GLCM),and Scale Invariant Feature Transform(SIFT),combined with classifiers such as Support Vector Machine(SVM),Random Forest(RF),as well as models like VGG16,ResNet101,InceptionV3,and MobileNetV2.The Results indicate that the classification accuracies of LBP_RF and GLCM_RF are 61.93%and 67.93%,respectively.The accuracy of the original dataset(SWFU LHB 10)on VGG16,ResNet101,InceptionV3,and MobileNetV2 reaches 70.90%,41.53%,76.10%,and 83.07%,respectively.However,performance declines after applying SIFT features.In contrast,combining Gabor features with MobileNetV2 significantly improves the classification accuracy to 98.94%,with an F1-score of 98.80%.Therefore,the proposed method based on 2D Gabor filtering and MobileNetV2 significantly outperforms other approaches in both feature extraction and model training.It provides an effective solution for longhorn beetle identification.