TY - GEN
T1 - Visual-Based Battery Labelling Quality Checker System Using Convolutional Neural Network
AU - Maulana, Muhammad Arif
AU - Istiqomah, Fivitria
AU - Musthofa, Arif
AU - Indasyah, Enny
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Having an auto labeling machine in the company is much faster and later determined by the company, but there are times when using an auto labeling machine results in incompatibility with the installation of battery labels. Apart from that, there are often claims from consumers that the label is not placed in the right place because the installation is done automatically. Based on this problem, we developed a machine that can detect the quality of label placement on batteries using machine vision. This machine vision technology is combined with the Convolutional Neural Network method. The system can detect label placement errors on batteries with a standard level of accuracy. The system can detect and classify three categories of battery label conditions with the average precision results for each class for no label batteries, rejected batteries and ok batteries respectively being 97.8%, 100% and 100%. The mean average precision (mAP) value produced by the detection model was 99.4%.
AB - Having an auto labeling machine in the company is much faster and later determined by the company, but there are times when using an auto labeling machine results in incompatibility with the installation of battery labels. Apart from that, there are often claims from consumers that the label is not placed in the right place because the installation is done automatically. Based on this problem, we developed a machine that can detect the quality of label placement on batteries using machine vision. This machine vision technology is combined with the Convolutional Neural Network method. The system can detect label placement errors on batteries with a standard level of accuracy. The system can detect and classify three categories of battery label conditions with the average precision results for each class for no label batteries, rejected batteries and ok batteries respectively being 97.8%, 100% and 100%. The mean average precision (mAP) value produced by the detection model was 99.4%.
KW - Convolutional Neural Network
KW - Customer Satisfaction
KW - Machine Vision
KW - Product Labelling
KW - Quality Checker
UR - http://www.scopus.com/inward/record.url?scp=85186496061&partnerID=8YFLogxK
U2 - 10.1109/ICAMIMIA60881.2023.10427850
DO - 10.1109/ICAMIMIA60881.2023.10427850
M3 - Conference contribution
AN - SCOPUS:85186496061
T3 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
SP - 881
EP - 886
BT - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023
Y2 - 14 November 2023 through 15 November 2023
ER -
