PCB Defect Detection Using Denoising Convolutional Autoencoders
- URL: http://arxiv.org/abs/2008.12589v1
- Date: Fri, 28 Aug 2020 11:38:09 GMT
- Title: PCB Defect Detection Using Denoising Convolutional Autoencoders
- Authors: Saeed Khalilian, Yeganeh Hallaj, Arian Balouchestani, Hossein
Karshenas, Amir Mohammadi
- Abstract summary: A small defect in Printed Circuit boards (PCBs) can cause significant flaws in the final product.
We propose an approach based on denoising convolutional autoencoders for detecting defective PCBs and to locate the defects.
- Score: 2.386911608328309
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Printed Circuit boards (PCBs) are one of the most important stages in making
electronic products. A small defect in PCBs can cause significant flaws in the
final product. Hence, detecting all defects in PCBs and locating them is
essential. In this paper, we propose an approach based on denoising
convolutional autoencoders for detecting defective PCBs and to locate the
defects. Denoising autoencoders take a corrupted image and try to recover the
intact image. We trained our model with defective PCBs and forced it to repair
the defective parts. Our model not only detects all kinds of defects and
locates them, but it can also repair them as well. By subtracting the repaired
output from the input, the defective parts are located. The experimental
results indicate that our model detects the defective PCBs with high accuracy
(97.5%) compare to state of the art works.
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