Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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SSD PCB Component Detection Using YOLOv5 Model

  • Pyeoungkee, Kim (Department of Computer Software Engineering, Silla University) ;
  • Xiaorui, Huang (Department of Computer Software Engineering, Silla University) ;
  • Ziyu, Fang (Department of Computer Software Engineering, Silla University)
  • Received : 2022.11.07
  • Accepted : 2023.01.30
  • Published : 2023.03.31
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Abstract

The solid-state drive (SSD) possesses higher input and output speeds, more resistance to physical shock, and lower latency compared with regular hard disks; hence, it is an increasingly popular storage device. However, tiny components on an internal printed circuit board (PCB) hinder the manual detection of malfunctioning components. With the rapid development of artificial intelligence technologies, automatic detection of components through convolutional neural networks (CNN) can provide a sound solution for this area. This study proposes applying the YOLOv5 model to SSD PCB component detection, which is the first step in detecting defective components. It achieves pioneering state-of-the-art results on the SSD PCB dataset. Contrast experiments are conducted with YOLOX, a neck-and-neck model with YOLOv5; evidently, YOLOv5 obtains an mAP@0.5 of 99.0%, essentially outperforming YOLOX. These experiments prove that the YOLOv5 model is effective for tiny object detection and can be used to study the second step of detecting defective components in the future.

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References

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