TY - GEN
T1 - A Design and Implementation of Stationery Product Recognition Method Using Two-Stage YOLO v8 Model
AU - Zhou, Xudong
AU - Funabiki, Nobuo
AU - Jing, Yanhui
AU - Xiao, Yanqi
AU - Kao, Wen Chung
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - A company typically maintains a lot of stationery products, such as ball-pens, glue-sticks, and erasers, for daily use in the operations. However, their accurate managements will cost a lot or often be impossible. Currently, YOLO is a very popular model for object recognition using images due to high capabilities. In this paper, we present a design and implementation of a stationery product recognition method using the latest YOLO v8 model at two stages. The first-stage model recognizes the category of the target object from the given image. The second-stage model selects the specific product in the category. For evaluations, we prepare 795 images of 45 stationery products in 9 categories and train the two-stage YOLO models using NVIDIA's RTX-3060 GPU. Then, we measure the training time and the recognition accuracy of the proposal. The results show the effectiveness of the proposal.
AB - A company typically maintains a lot of stationery products, such as ball-pens, glue-sticks, and erasers, for daily use in the operations. However, their accurate managements will cost a lot or often be impossible. Currently, YOLO is a very popular model for object recognition using images due to high capabilities. In this paper, we present a design and implementation of a stationery product recognition method using the latest YOLO v8 model at two stages. The first-stage model recognizes the category of the target object from the given image. The second-stage model selects the specific product in the category. For evaluations, we prepare 795 images of 45 stationery products in 9 categories and train the two-stage YOLO models using NVIDIA's RTX-3060 GPU. Then, we measure the training time and the recognition accuracy of the proposal. The results show the effectiveness of the proposal.
KW - YOLO model
KW - object recognition
KW - stationery product
KW - two stages
UR - http://www.scopus.com/inward/record.url?scp=85196058630&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85196058630&partnerID=8YFLogxK
U2 - 10.1109/ICIET60671.2024.10542804
DO - 10.1109/ICIET60671.2024.10542804
M3 - Conference contribution
AN - SCOPUS:85196058630
T3 - 2024 12th International Conference on Information and Education Technology, ICIET 2024
SP - 327
EP - 332
BT - 2024 12th International Conference on Information and Education Technology, ICIET 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Information and Education Technology, ICIET 2024
Y2 - 18 March 2024 through 20 March 2024
ER -