Development of PCB Defect Detection System Using Image Processing With YOLO CNN Method

(1) * Agus Dwi Santoso Mail (Politeknik Pelayaran Surabaya, Indonesia)
(2) Ferry Budi Cahyono Mail (Politeknik Pelayaran Surabaya, Indonesia)
(3) Brendi Prahasta Mail (Politeknik Perkapalan Negeri Surabaya, Indonesia)
(4) Imam Sutrisno Mail (Politeknik Perkapalan Negeri Surabaya, Indonesia)
(5) Agus Khumaidi Mail (Politeknik Perkapalan Negeri Surabaya, Indonesia)
*corresponding author

Abstract


Inside the equipment there are many electronic components such as resistors, transistors, capacitors and so on. When used in the production of electronic equipment, PCBs are very influential in the manufacture of these electronic devices, for example, when there are only a few broken or damaged PCB paths, the electronic device cannot be operated properly. So it is very important in the PCB Quality Check process to check whether there is damage to the PCB or not. Usually in PCB inspection only direct checking is used in the conventional way. Therefore, in this study, the author tries to create and analyze a PCB flaw checking tool with the help of a camera that has a high revolution to replace human vision to make it easier and save costs. The application of this PCB checking tool uses a technology called a laptop and a camera. With these two technologies, Image Processing can be used to detect objects using the OpenCv and Tensorflow libraries. PCB flaw detection tool with the help of Image Processing with the YOLO Convolutional Neural Network method to help determine broken paths and drill holes on the PCB

Keywords


YOLO CNN, image processing, PCB

   

DOI

https://doi.org/10.29099/ijair.v6i1.343
      

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References


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