TY - GEN
T1 - Convolutional neural network for industrial egg classification
AU - Shimizu, Ryota
AU - Yanagawa, Shusuke
AU - Shimizu, Toru
AU - Hamada, Mototsugu
AU - Kuroda, Tadahiro
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/5/29
Y1 - 2018/5/29
N2 - CNN (Convolutional Neural Network) is a powerful method for image classifying tasks. CNN's classifying capability is assessed by using large-scale image dataset such as ImageNet in many papers, but few works on CNN with small-scale dataset have been reported. We have been researching application method of Neural Network for classifying tasks in real-world for years [1]. In this work, we applied CNN to a quality inspection of industrial products and assessed its classifying capacity. Our CNN was trained with 2000 images of eggs taken in a factory, classified the images of almost 89,000 eggs into 6 qualities. Our method of combining multi-angle images into 1 image retained the 3-dimensional features of the object, and improved the classification accuracy to 92.3%. It confirmed that CNN is also effective for the quality inspection of industrial products.
AB - CNN (Convolutional Neural Network) is a powerful method for image classifying tasks. CNN's classifying capability is assessed by using large-scale image dataset such as ImageNet in many papers, but few works on CNN with small-scale dataset have been reported. We have been researching application method of Neural Network for classifying tasks in real-world for years [1]. In this work, we applied CNN to a quality inspection of industrial products and assessed its classifying capacity. Our CNN was trained with 2000 images of eggs taken in a factory, classified the images of almost 89,000 eggs into 6 qualities. Our method of combining multi-angle images into 1 image retained the 3-dimensional features of the object, and improved the classification accuracy to 92.3%. It confirmed that CNN is also effective for the quality inspection of industrial products.
KW - Convolutional Neural Network
KW - Image Classification
KW - Quality Inspection
KW - Small-scale Dataset
UR - http://www.scopus.com/inward/record.url?scp=85048859630&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048859630&partnerID=8YFLogxK
U2 - 10.1109/ISOCC.2017.8368830
DO - 10.1109/ISOCC.2017.8368830
M3 - Conference contribution
AN - SCOPUS:85048859630
T3 - Proceedings - International SoC Design Conference 2017, ISOCC 2017
SP - 67
EP - 68
BT - Proceedings - International SoC Design Conference 2017, ISOCC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International SoC Design Conference, ISOCC 2017
Y2 - 5 November 2017 through 8 November 2017
ER -