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

Bokuan Yang; Yuhu Nie; Wenpeng Cui; Jian Sun; Hao Lu; Wei Su

doi:10.1117/12.2660551

30 November 2022 Generative adversarial network for PCB defect detection with extreme low compress rate

Bokuan Yang, Yuhu Nie, Wenpeng Cui, Jian Sun, Hao Lu, Wei Su

Proceedings Volume 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022); 124561K (2022) https://doi.org/10.1117/12.2660551
Event: International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 2022, Qingdao, China

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.

Citation Download Citation

Bokuan Yang, Yuhu Nie, Wenpeng Cui, Jian Sun, Hao Lu, and Wei Su "Generative adversarial network for PCB defect detection with extreme low compress rate", Proc. SPIE 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 124561K (30 November 2022); https://doi.org/10.1117/12.2660551

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KEYWORDS

Defect detection

Image compression

Gallium nitride

Network architectures

Data modeling

Manufacturing

Neural networks

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