Name
Abstract | ||
|---|---|---|
Detecting packaging defection with high accuracy and efficiency is of great significance in product quality. We use OpenCV to preprocess images which come from damaged package according to characteristics of the image. The processed data is combined with deep learning and based on neural network model ResNet. Meanwhile the processed image data is sent to a convolutional neural network (CNN) for model training. We establish a detection system for product packaging. The detection system provides a solution for automatic detection of package defection, which realizes rapid and accurate detection of product packaging. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICCCS49078.2020.9118413 | 2020 5th International Conference on Computer and Communication Systems (ICCCS) |
Keywords | DocType | ISBN |
packaging defection detection,image preprocessing,convolution neural network,TensorRT,ResNet,Keras | Conference | 978-1-7281-6137-2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiming Sa | 1 | 0 | 0.34 |
| Zhihao Li | 2 | 136 | 17.95 |
| Qijun Yang | 3 | 0 | 0.34 |
| Xuan Chen | 4 | 0 | 0.34 |