PCB Defect Detection Using Denoising Convolutional Autoencoders

TL;DR

This paper introduces a denoising convolutional autoencoder approach for detecting, locating, and repairing defects in printed circuit boards, achieving high accuracy and outperforming existing methods.

Contribution

The paper presents a novel autoencoder-based method that detects, locates, and repairs PCB defects simultaneously, improving defect detection accuracy over prior techniques.

Findings

Detects PCB defects with 97.5% accuracy

Locates defects by subtracting repaired images from original

Outperforms state-of-the-art defect detection methods

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.