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Research on Vehicle and Pedestrian Detection Based on Improved RT-DETR Cover

Research on Vehicle and Pedestrian Detection Based on Improved RT-DETR

By: Jingshu LI and  Jianguo Wang  
Open Access
|Jun 2025

Figures & Tables

Figure 1.

Network architecture of RT-DETR
Network architecture of RT-DETR

Figure 2.

Network architecture of improved RT-DETR
Network architecture of improved RT-DETR

Figure 3.

Full 3-D weights for attention
Full 3-D weights for attention

Figure 4.

Comparison before and after improvement
Comparison before and after improvement

Figure 5.

Visualization results
Visualization results

EXPERIMENTAL RESULTS

RT-DETRWithout SimAMAdded SimAM
Precision0.7790.793
Recall0.6210.624
mAP@500.6990.736
mAP@50:590.3790.383

EXPERIMENTAL PLATFORM

Hyper-parametersValue
Inputs640×640
Epochs100
Batchsize16
Lr00.001
Lrf0.0001
Momentum0.9
Warmup-decay0.0005
Warmup-epochs5

DATASER SAMPLING SITUATION

ContentDetailed information
Dataset size69534 valid training samples
Sample methodRandomly select samples
Sample quantitySampling 3000 samples
Tag filteringFilter other category tags
Division ratio8:2
Training2400 training images
Verify600 verification images

CAMPARISON RESULTS

ModelYOLOv8Ours
Precision0.7210.793
Recall0.6450.624
mAP@500.6920.736
mAP@50:590.3720.383
Language: English
Page range: 85 - 93
Published on: Jun 16, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Jingshu LI, Jianguo Wang, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.