Cleanliness classification model for tobacco conveyor belt based on an improved residual network
Addressing the current reliance on manual subjective judgment and strong subjectivity for assessing the cleanliness of tobacco conveyor belts,a cleanliness classification model(ResNet24_SC_Block)for tobacco conveyor belt using an improved ResNet was proposed.This model uses ResNet for classification with a network depth of 24 layers.SE and CBAM attention mechanisms were introduced into the residual module to improve the model's ability to capture features such as conveyor belt color and adhesion smoke scale.Using the tobacco leaf conveyor belt data-set to experiment with this model,the experimental results showed that the average values of Accuracy,Precision,Recall and Fl of the improved ResNet24_SC_Block model were 98.8％,98.8％and 98.4％,respectively,which were 3.3％～3.8％higher than those of ResNet18 model and ResNet34 model.Compared with classic and newer net-works such as GoogLeNet model and RegNet model,it improves by 2.1％to 17.9％.And the number of model parameters was reduced by 31.6％compared with ResNet34 model.This approach offered notable advantages in accu-rately and efficiently assessing the cleanliness level of tobacco conveyor belts,making it highly consequential and practi-cally valuable for intelligent upgrades in cigarette manufacturing plants.
万方数据
维普
