PCB Defect Detection Using Denoising Convolutional Autoencoders
This addresses quality control in electronics manufacturing by improving defect detection, though it appears incremental as it applies an existing method to a specific domain.
The paper tackles the problem of detecting and locating defects in printed circuit boards (PCBs) by using denoising convolutional autoencoders, achieving a detection accuracy of 97.5% compared to state-of-the-art methods.
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.