基于改进ResNet18模型的异性纤维识别分类方法研究OA

Aiming at the problems of lower efficiency and insufficient accuracy for manual detecting foreign fibers in cotton,a deep learning method based on improved ResNet18 was proposed to realize automatic and accurate recognization of foreign fibers,improve the quality control level of textiles.Combined with rotation,scale,shift and other foreign fiber image fusion,robustness of the improved ResNet18 model to complex scenes was improved.By adding 1×1 convolutional neural network to skip connection channels,the problem of deep network gradient disappearance was solved,feature fusion ability of the improved ResNet18 model was enhanced.Adam optimizer was used to dynamically adjust the learning rate and accelerate convergence of the improved ResNet18 model,so that the accuracy rate on foreign fiber test set was reached 99.9%,which was 4.8 percentage points higher than the original ResNet18 model.The accuracy and efficiency of foreign fiber detection was increased by improved ResNet18 model effectively through structural optimization and data enhancement,which provided an intelligent foreign fiber detection scheme for textile industry.