Loading...
Please wait, while we are loading the content...
Similar Documents
Surface Flaw Detection of Industrial Products Based on Convolutional Neural Network
| Content Provider | Scilit |
|---|---|
| Author | Zhang, Yongjun Wang, Ziliang |
| Copyright Year | 2019 |
| Description | Journal: Iop Conference Series: Earth and Environmental Science Surface flaw detection in industrial products is a typical application of image classification. By improving the structure of Convolutional Neural Network (CNN), for example, the first large-scale convolution kernel is replaced by a cascaded 3×3 convolution kernel; replaces the whole with a 1×1 convolution kernel and Global Average Pooling Connection layer; sets the appropriate batch_size, the convergence rate and convergence accuracy of the model are greatly improved. Experiments show that the proposed method has a classification accuracy of more than 96% in the detection of automotive hose surface flaws. |
| Related Links | https://iopscience.iop.org/article/10.1088/1755-1315/252/2/022114/pdf |
| ISSN | 17551307 |
| e-ISSN | 17551315 |
| DOI | 10.1088/1755-1315/252/2/022114 |
| Journal | Iop Conference Series: Earth and Environmental Science |
| Issue Number | 2 |
| Volume Number | 252 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2019-07-09 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Earth and Environmental Science Industrial Engineering Industrial Products Convolutional Neural |
| Content Type | Text |
| Resource Type | Article |
| Subject | Earth and Planetary Sciences Physics and Astronomy Environmental Science |
