Open AccessDesign of printed circuit board detection with image information technology
Author(s) -
Tibyani,
Nur Hayati,
Imam Sutrisno,
Agus Khumaidi,
Md. Atiqur Rahman,
Fachrul Muhammad Gumelar,
K. Dhipanusa,
Filemon,
Eko Supriyanto
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1175/1/012011
Subject(s) - printed circuit board , artificial intelligence , classifier (uml) , haar like features , computer science , haar , computer vision , cascade , engineering , pattern recognition (psychology) , face detection , chemical engineering , wavelet , facial recognition system , operating system
In this research, an automatic PCB’s deffects checking tool will be made with image processing technology that adopts Haar Cascade Classifier method as a decision maker for whether or not a broken circuit on the PCB, with this technology the PCB will be detected using a square feature to show the broken path. will be displayed on the interface on the monitor. From the results of the test, data using the Haar Casscade Classifier method has the average percentage of successful detection of broken paths on the PCB at a camera distance of 26 cm and 16 cm is 52.49% with the maximum hassle obtained when the camera distance is placed at a distance of 26 cm and an angle position of 105 degrees which has a success rate of 97.5%. This method is good enough for PCB defect detection tools.