Generative adversarial network for PCB defect detection with extreme low compress rate

Abstract

Printed circuit board (PCB) manufacturing is one of the most important parts of electronic production, where a small defect may cause the final product to fail. Therefore, the industry urgently needs a system to detect and locate all manufacturing defects. In this paper, we propose Generative Adversarial Networks (GANs) based learning defect system with an extremely low bit per pixel (BPP) for feature compression. The system includes an encoder, generator, and multi-scale discriminator for generative learned compression and a comparator to distinguish defected components from a compressed feature map generated by GAN. The model synthesizes images at extreme low bitrates where traditional methods such as JPEG show strong artifacts, resulting in a proportional reduction in the storage of feature comparison. Experimental results demonstrate the effectiveness and efficiency of our model with 97.8% mAP at 72FPS.

An Enhanced Detection Method of PCB Defect Based on D-DenseNet (PCBDD-DDNet)

“…These samples are intentionally designed to deceive the model, enabling it to remain effective in the presence of different types of defects or anomalies. This means introducing adversarial samples during model training to ensure its effectiveness when facing various types of defects or anomalies [36][37][38]. Hashing is an efficient method for nearest neighbor search in large-scale data space by embedding high-dimensional feature descriptors into a similarity preserving Hamming space with a low dimension [39].…”