Name
Title | ||
|---|---|---|
Image Classification for Automobile Pipe Joints Surface Defect Detection Using Wavelet Decomposition and Convolutional Neural Network |
Abstract | ||
|---|---|---|
The surface defect detection of automobile pipe joints based on computer vision faces technical challenges. The tiny-sized and smooth surfaces with processing textures will undermine the defect detection accuracy. In order to solve this problem, a new method was proposed, which combines wavelet decomposition and reconstruction with the canny operator to detect defects, and then uses the multi-channel fusion convolutional neural network to identify the types of defects. Firstly, illumination compensation technology is used to obtain a more uniform gray distribution of the original image. Then, the wavelet decomposition and reconstruction are used to remove noises and processing textures. Furthermore, the defect regions are segmented using the canny operator and hole filling from the image. Finally, the multi-channel fusion convolutional neural network of decision-level is used to identify the surface defect types. This method provides an idea for the surface defects detection of automobile pipe joints with serious interference, such as smooth surface, random noises, and processing textures. The experimental results reveal that the method can effectively eliminate the influence of uneven illumination, random noises, and processing textures and achieve high defect classification accuracy. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ACCESS.2022.3178380 | IEEE ACCESS |
Keywords | DocType | Volume |
Surface treatment, Surface waves, Convolutional neural networks, Automobiles, Surface texture, Surface reconstruction, Feature extraction, Automobile pipe joint, surface defect detection, wavelet decomposition and reconstruction, multi-channel fusion convolutional neural network | Journal | 10 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeqing Yang | 1 | 0 | 0.68 |
| Mingxuan Zhang | 2 | 0 | 0.34 |
| Chao Li | 3 | 525 | 110.37 |
| Zhaozong Meng | 4 | 3 | 2.24 |
| Yue Li | 5 | 6 | 10.29 |
| Yingshu Chen | 6 | 0 | 0.34 |
| Libing Liu | 7 | 0 | 0.34 |