Cigar image recognition and counting based on convolutional neural networks
[Background]This study aims to solve the problems of cumbersome and time-consuming manual counting of cigars.[Methods]Using different shaped finished cigars as subjects,cigar end images were obtained through charge-coupled devices.The high-level features of the cigar images were extracted using the representation learning function of convolutional neural networks.The cigars in the images were manually annotated,and an instance segmentation model for cigar image recognition and counting was established through training with a large number of sample images.[Results]The cigar image recognition and counting model can effectively identify various shapes of cigars and cigars for special purposes,with an accuracy rate of 99.04%.The efficiency of image recognition and counting is 169.53%higher than that of manual counting.[Conclusion]The model and method established in this study have good practicability in cigar recognition for warehousing and counting verification.
cigarscharge-coupled deviceconvolutional neural networkimage recognition technologyimage recognition counting model
万方数据
维普
