Color Quality Classification System for Chemical Fibers Based on Improved EfficientNet
Chemical filament long silk is one of the common textile raw materials.In order to ensure the quality of chemical filament spools,a thorough inspection must be carried out before leaving the factory.In this paper,a system for classifying the appearance quality of the chemical filament spools is designed,which enables rapid detection of the chemical filament spools.First,the experimental platform is modeled and constructed,and then four types of chemical filament spool photos are collected in the field and converted into a chemical color quality classification dataset using data augmentation.Furthermore,to better integrate the multiscale information of chemical images,the PANet structure is introduced to replace the FPN layer.Additionally,to ensure that the network model focused on the key features during the multiscale fusion,the PSA attention mechanism is introduced into the PANet.The experimental results show that this method achieved an accuracy of 99.87%on the current dataset with improvements in precision and recall compared with ResNet,VGG,and DenseNet,and can effectively classify the chemical color quality.
chemical fibertestingattention mechanismdata enhancementEfficientNet
NETL
NSTL
万方数据
