TY - GEN
T1 - Improved MLP-Mixer for Cars' Type Recognition
AU - Cao, Bin
AU - Ma, Hongbin
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - With the development of intelligent manufacturing, the automotive industry, which is an important part of national economy, has attracted attention widely once again and the cars' type recognition is a crucial part for the automotive industry. The accuracy of cars' type recognition will directly affect the painting and other operations. Therefore, the car's type recognition requires a high accuracy over 99%. Some papers propose deep learning models for the cars' type recognition. However, many existing deep learning models have problems such as requirements for massive samples, slow convergence and difficulty in achieving the accuracy over 99%, which makes them have a little application to the industry. This paper takes the cars' type recognition as the background and adds the prior knowledge to the deep learning model, which introduces LBP into MLP-Mixer to improve the accuracy effectively.
AB - With the development of intelligent manufacturing, the automotive industry, which is an important part of national economy, has attracted attention widely once again and the cars' type recognition is a crucial part for the automotive industry. The accuracy of cars' type recognition will directly affect the painting and other operations. Therefore, the car's type recognition requires a high accuracy over 99%. Some papers propose deep learning models for the cars' type recognition. However, many existing deep learning models have problems such as requirements for massive samples, slow convergence and difficulty in achieving the accuracy over 99%, which makes them have a little application to the industry. This paper takes the cars' type recognition as the background and adds the prior knowledge to the deep learning model, which introduces LBP into MLP-Mixer to improve the accuracy effectively.
KW - Cars' type recognition
KW - LBP
KW - MLP-Mixer
UR - http://www.scopus.com/inward/record.url?scp=85149548786&partnerID=8YFLogxK
U2 - 10.1109/CCDC55256.2022.10033636
DO - 10.1109/CCDC55256.2022.10033636
M3 - Conference contribution
AN - SCOPUS:85149548786
T3 - Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022
SP - 6040
EP - 6045
BT - Proceedings of the 34th Chinese Control and Decision Conference, CCDC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th Chinese Control and Decision Conference, CCDC 2022
Y2 - 15 August 2022 through 17 August 2022
ER -