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Pavement Damage Recognition Based on Deep Learning Cover
By: Mingbo Ning and  Shengquan Yang  
Open Access
|Jun 2025

Figures & Tables

Figure 1.

Road surface damage dataset under different conditions
Road surface damage dataset under different conditions

Figure 2.

RT-DETR-r18 model structure
RT-DETR-r18 model structure

Figure 3.

Improved RT-DETR model structure
Improved RT-DETR model structure

Figure 4.

Diagrams of different types of cracks
Diagrams of different types of cracks

Figure 5.

Structure of LMBA module
Structure of LMBA module

Figure 7.

Structure of EFKM module
Structure of EFKM module

Figure 6.

Comparison of feature Pyramid net
Comparison of feature Pyramid net

Figure 8.

Comparison chart of mAP during training
Comparison chart of mAP during training

Figure 9.

Average precision of each label in RT-DETR
Average precision of each label in RT-DETR

Figure 10.

Average precision of each label in Improved RT-DETR
Average precision of each label in Improved RT-DETR

Figure 11.

Visual comparison of test results
Visual comparison of test results

COMPARISON BEFORE AND AFTER IMPROVEMENT

AlgorithmPars/MFLOPS/GFPS/s/fmAP/%
RT-DETR19.857.36967.1
Yolov11m20.168.010767.9
Fast-RCNN136.5370.22150.2
Improved RT-DETR14.645.26069.2

EXPERIMENTAL ENVIRONMENT

Experimental environmentVersion
CPUIntel Xeon Platinum 8352V
GPUNVIDIA GeForce RTX4090D
LanguagePython3.9
Deep Learning FrameworkPytorch1.13.1
CUDA11.6.0

DISEASE CATEGORY

CategoryTrain SetTest Set
D00(Longitudinal cracks)7419876
D10(Transverse cracks)5702636
D20(Alligator cracks)6244689
D40(Potholes)2316248
Language: English
Page range: 74 - 84
Published on: Jun 16, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Mingbo Ning, Shengquan Yang, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.