TY - GEN
T1 - A VGG-16 Based Faster RCNN Model for PCB Error Inspection in Industrial AOI Applications
AU - Li, Yu Ting
AU - Guo, Jiun-In
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/27
Y1 - 2018/8/27
N2 - To detect product error and modify the product error, most industry are using human eyes. However, it is not only costs time but also costs money. Our purpose is to develop a model to detect the PCB board errors and draw the bounding boxes. The model is going to be developed with a pre-trained model VGG16 and data collected from Adventech corp. The error types of training data have been speared into five error types (Bridge, Appearance, Empty, Solder-ball, Solder-balls), where the highest AP result of these classes is over 90%.
AB - To detect product error and modify the product error, most industry are using human eyes. However, it is not only costs time but also costs money. Our purpose is to develop a model to detect the PCB board errors and draw the bounding boxes. The model is going to be developed with a pre-trained model VGG16 and data collected from Adventech corp. The error types of training data have been speared into five error types (Bridge, Appearance, Empty, Solder-ball, Solder-balls), where the highest AP result of these classes is over 90%.
UR - http://www.scopus.com/inward/record.url?scp=85053931656&partnerID=8YFLogxK
U2 - 10.1109/ICCE-China.2018.8448674
DO - 10.1109/ICCE-China.2018.8448674
M3 - Conference contribution
AN - SCOPUS:85053931656
SN - 9781538663011
T3 - 2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
BT - 2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
Y2 - 19 May 2018 through 21 May 2018
ER -