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Intelligent capture analysis model for high-speed toll evasion vehicles based on vehicle re-identification algorithm

Sinan Song

Sinan Song

Hebei Expressway Group Limited Qingyin BranchShijiazhuang, China
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Song, Sinan
  
Mar 17, 2025
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Published Online: Mar 17, 2025
Received: Oct 22, 2024
Accepted: Feb 15, 2025
DOI: https://doi.org/10.2478/amns-2025-0181
Keywords
© 2025 Sinan Song, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Schematic model of vehicle recognition algorithm based on multi-resolution self-attention
Schematic model of vehicle recognition algorithm based on multi-resolution self-attention

Figure 2.

Multi-scale spatial attention module structure
Multi-scale spatial attention module structure

Figure 3.

Channel attention structure
Channel attention structure

Figure 4.

Schematic model of vehicle re-identification method based on multi-dimensional feature fusion
Schematic model of vehicle re-identification method based on multi-dimensional feature fusion

Figure 5.

The structure of multi-scale feature extraction module
The structure of multi-scale feature extraction module

Figure 6.

Schematic of comparison experiments on VeRI-776 and VehicleID datasets
Schematic of comparison experiments on VeRI-776 and VehicleID datasets

Figure 7.

Evaluation indicators and loss functions
Evaluation indicators and loss functions

Figure 8.

Comparative experiments of various methods on VeRI-776 and VehicleID
Comparative experiments of various methods on VeRI-776 and VehicleID

Hardware, software, and parameter settings for the experiment

Name Imprint
Systems ubuntu18.04 LTS
CUDA 10.2
cuDNN 7.6.5
Programming languages Python3.7.7
Deep learning frameworks Pytorch
CPU Intel(R)Core(TM)i7-12700H
GPU RTX 3090Ti
Display memory 24GB
Random access memory 64GB
Hard disk 2TB
Optimizers Adam

Fusion experiment of multi-scale attention mechanism and multi-scale information fusion vehicle recognition algorithm on VeRI-776 and VehicleID

VehicleID dataset VeRI-776 dataset
Methodologies Small dataset Medium dataset Large dataset mAP Rank-1 Rank-5
mAP Rank-1 mAP Rank-1 mAP Rank-1
Baseline 82.67 92.31 88.12 90.26 77.29 95.84 82.36 92.17 94.67
Baseline+multi-scale attention mechanism 86.22 95.14 88.2 90.24 77.84 90.59 83.15 93.25 95.49
Baseline+multi-scale information fusion 84.57 97.12 88.11 95.12 74.48 91.13 83.64 94.36 96.17
Multi-scale attention mechanism+multi-scale information fusion 89.23 98.65 88.26 96.16 77.97 94.62 84.69 97.64 98.15
Language:
English
Publication timeframe:
1 times per year
Journal Subjects:
Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other
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