Classification of cigarette appearance defects based on ResNeSt

In the product quality inspection of cigarette factories, automatic classification of cigarette appearance defect is a problem that needs to be solved on high-speed assembly lines. It is an effective measure to improve the quality and competitiveness of cigarettes. Deep learning is used to classify cigarette appearance defects, and a classification method of cigarette appearance defect based on ResNeSt is proposed in this paper. Firstly, the transfer learning is adopted to solve insufficient samples of cigarette appearance defects. Secondly, according to the characteristics of cigarette images, multi-scale testing is used, and different scales images are inputted to train. Finally, h-swish is used to replace the ReLU activation function in order to better extract the defect features and improve the classification accuracy. The experimental results show that the proposed method has a higher classification accuracy than the other 10 mainstream networks.