  Intelligent Transportation Systems (ITS) utilize sensors, cameras, and big
data analysis to monitor real-time traffic conditions, aiming to improve
traffic efficiency and safety. Accurate vehicle recognition is crucial in this
process, and Vehicle Logo Recognition (VLR) stands as a key method. VLR enables
effective management and monitoring by distinguishing vehicles on the road.
Convolutional Neural Networks (CNNs) have made impressive strides in VLR
research. However, achieving higher performance demands significant time and
computational resources for training. Recently, the rise of Transformer models
has brought new opportunities to VLR. Swin Transformer, with its efficient
computation and global feature modeling capabilities, outperforms CNNs under
challenging conditions. In this paper, we implement real-time VLR using Swin
Transformer and fine-tune it for optimal performance. Extensive experiments
conducted on three public vehicle logo datasets (HFUT-VL1, XMU, CTGU-VLD)
demonstrate impressive top accuracy results of 99.28%, 100%, and 99.17%,
respectively. Additionally, the use of a transfer learning strategy enables our
method to be on par with state-of-the-art VLR methods. These findings affirm
the superiority of our approach over existing methods. Future research can
explore and optimize the application of the Swin Transformer in other vehicle
vision recognition tasks to drive advancements in ITS.
