International Journal of Industrial Electronics and Electrical Engineering(IJIEEE).
Paper Title - Surf Based Fault Image Detection For Printed Circuit Board Inspection
It is possible to collect, manage and analyze large amounts of data in real time because of development of fusion
technology and spread of Internet of Things(IoT) in modern society. With the advanced technology, the high-tech product
manufacturers began to offer differentiated services by producing fewer customized products. According to this, PCB
(Printed Circuit Board, PCB), key component of the digital products, is also produced in small quantity batch production.
Current PCB inspection systems require information about the normal image because it detects faults by comparison with the
non-defective image. Therefore, this means that the test is not possible without the normal image and needs to have the all
reference image. It is a major cause of reducing the efficiency of the PCB inspection system. This paper proposes a method
for detecting the fault in PCB without normal image by learning the pattern of abnormal image. As a result of this
methodology, it is expected to check more effectively the defects in the system to produce a variety of products and bring the
time and cost savings in PCB inspection.
Keywords— PCB Inspection, Speeded Up Robust Feature, Random Forest, Kernel Density Estimation.
Author - Eun-Hye Yuk, Seongwon Jang, Seunghwan Park, Cheong-Sool Park, Jun-Geolbaek