Open AccessResearch on Vehicle Appearance Component Recognition Based on Mask R-CNN
Author(s) -
Qiaoming Zhu,
Sen Liu,
Wei Guo
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1335/1/012026
Subject(s) - artificial intelligence , computer science , process (computing) , component (thermodynamics) , position (finance) , range (aeronautics) , computer vision , image (mathematics) , pattern recognition (psychology) , engineering , physics , finance , economics , thermodynamics , aerospace engineering , operating system
Recognition of vehicle exterior components is one of the most important core algorithms in the process of intelligent propulsion. This paper focuses on the recognition algorithm of vehicle appearance parts, which can detect the position of vehicle appearance parts and recognize the name of the parts in the image. This paper applies enterprise self-built datasets. Firstly, the vehicle close-range dataset is produced by tailoring in data enhancement. Secondly, the models based on ResNeXt-50+FPN, ResNeXt-101+FPN, ResNeXt-50 backbone network and Mask R-CNN are applied in three stages: panoramic dataset, panoramic dataset and panoramic close-range integrated dataset. Finally, the network model based on ResNeXt-101+FPN and Mask R-CNN is the best on the comprehensive dataset.