Automatic identification method of cotton/wool fibers based on transfer learning
Aiming at the problems of low efficiency and strong subjectivity in manual classification of textile fibers,an automatic identification method for cotton and wool fibers in textile wastes under the condition of small samples was proposed.Firstly,scanning electron microscope was used to photograph the existing cotton fiber and form a small sample of cotton fiber image set.Then,four kinds of models trained by ImageNet data set were loaded for transfer learning,network parameters of the models were retained or partially fine-tuned,and the classification model was generated based on the small sample image set for training and verification.Finally,based on the evaluation indexes of accuracy,accuracy and recall rate,various classification models were compared and tested,and the optimal classification model was selected to realize the automatic recognition of cotton fiber.The experimental results show that the ResNetXt50 model achieves the highest accuracy rate in the process of model training,and its value is 97.33%.The test set is tested,and the results show that the test accuracy of ResNet50 and ResNetXt50 can reach 99.537%among the four kinds of classification models after fine-tuning,which verifies the effectiveness of the method.
NETL
NSTL
万方数据
