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Detection of PCB Components Using Deep Neural Nets  

Cho, Tai-Hoon (School of Computer Science and Engineering, Korea University of Technology and Education)
Publication Information
Journal of the Semiconductor & Display Technology / v.19, no.2, 2020 , pp. 11-15 More about this Journal
Abstract
In a typical initial setup of a PCB component inspection system, operators should manually input various information such as category, position, and inspection area for each component to be inspected, thus causing much inconvenience and longer setup time. Although there are many deep learning based object detectors, RetinaNet is regarded as one of best object detectors currently available. In this paper, a method using an extended RetinaNet is proposed that automatically detects its component category and position for each component mounted on PCBs from a high-resolution color input image. We extended the basic RetinaNet feature pyramid network by adding a feature pyramid layer having higher spatial resolution to the basic feature pyramid. It was demonstrated by experiments that the extended RetinaNet can detect successfully very small components that could be missed by the basic RetinaNet. Using the proposed method could enable automatic generation of inspection areas, thus considerably reducing the setup time of PCB component inspection systems.
Keywords
Deep Neural Networks; Object Detection; PCB Component Inspection; RetinaNet;
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Times Cited By KSCI : 2  (Citation Analysis)
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