Name
Title | ||
|---|---|---|
Image Classification and Detection of Cigarette Combustion Cone Based on Inception Resnet V2 |
Abstract | ||
|---|---|---|
In order to guide the production of cigarette products and improve the quality of cigarette products, this paper proposes a classification method for cigarette combustion cones based on deep convolutional neural network model. The method is optimized based on the Inception Resnet V2 model and is innovatively used in the detection of cigarette burning cones. The classification accuracy of combustion cone fallout is characterized by the overall classification accuracy (OA) and the Kappa coefficient (Kappa). The experimental results show that the overall classification accuracy is 97.22%, and the Kappa coefficient is 0.9583. The deep convolutional neural network has better classification effect. Based on the classification method of deep convolutional neural network, the cigarette burning cone can be accurately identified. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICCCS49078.2020.9118570 | 2020 5th International Conference on Computer and Communication Systems (ICCCS) |
Keywords | DocType | ISBN |
image classification,cigatette combustion cone,data augmentation,Inception Resnet V2,Fine-tuning | Conference | 978-1-7281-6137-2 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoqing Deng | 1 | 0 | 0.34 |
| Yangguang Zhao | 2 | 0 | 0.34 |
| Long Zhang | 3 | 0 | 0.34 |
| Zhigang Li | 4 | 34 | 11.35 |
| Yong Liu | 5 | 0 | 1.69 |
| Yi Zhang | 6 | 37 | 10.48 |
| Li B | 7 | 14 | 4.94 |