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All Journal International Journal of Artificial Intelligence Research
Ferry Budi Cahyono
Politeknik Pelayaran Surabaya
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Artificial Intelligence Research

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Development of PCB Defect Detection System Using Image Processing With YOLO CNN Method Agus Dwi Santoso; Ferry Budi Cahyono; Brendi Prahasta; Imam Sutrisno; Agus Khumaidi
International Journal of Artificial Intelligence Research Vol 6, No 1.1 (2022)
Publisher : International Journal of Artificial Intelligence Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (212.907 KB) | DOI: 10.29099/ijair.v6i1.343

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
Co-Authors Agus Dwi Santoso Agus Khumaidi Brendi Prahasta Imam Sutrisno