| Autor: |
Hsu, Quang Cherng, Jhou, Yu Sin, Ye, Jhan Hong, Ma, Chen Wei, Lai, You Rui |
| Zdroj: |
Materials Science Forum; December 2023, Vol. 1109 Issue: 1 p173-178, 6p |
| Abstrakt: |
The paper proposed a deep convolutional neural network together with image processing techniques to detect assembly defects of vehicle components in assembly lines. Traditional detection method such as automatic optical inspection is strongly affected by environmental variation coming from the changes of light source, transfer belt, and component type, therefore, complicated thresholds should be adjusted case by case. The proposed method tries to avoid these problems which is fast and straight forward with satisfactory detection accuracy compared to traditional method. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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