INFORMAZIONI SU QUESTO ARTICOLO
Pubblicato online: 16 giu 2025
Pagine: 74 - 84
DOI: https://doi.org/10.2478/ijanmc-2025-0018
Parole chiave
© 2025 Mingbo Ning et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Figure 1.
Figure 2.
Figure 3.
Figure 4.
Figure 5.
Figure 7.
Figure 6.
Figure 8.
Figure 9.
Figure 10.
Figure 11.
COMPARISON BEFORE AND AFTER IMPROVEMENT
| Algorithm | Pars/M | FLOPS/G | FPS/s/f | mAP/% |
|---|---|---|---|---|
| RT-DETR | 19.8 | 57.3 | 69 | 67.1 |
| Yolov11m | 20.1 | 68.0 | 107 | 67.9 |
| Fast-RCNN | 136.5 | 370.2 | 21 | 50.2 |
| Improved RT-DETR | 14.6 | 45.2 | 60 | 69.2 |
EXPERIMENTAL ENVIRONMENT
| Experimental environment | Version |
|---|---|
| CPU | Intel Xeon Platinum 8352V |
| GPU | NVIDIA GeForce RTX4090D |
| Language | Python3.9 |
| Deep Learning Framework | Pytorch1.13.1 |
| CUDA | 11.6.0 |
DISEASE CATEGORY
| Category | Train Set | Test Set |
|---|---|---|
| D00(Longitudinal cracks) | 7419 | 876 |
| D10(Transverse cracks) | 5702 | 636 |
| D20(Alligator cracks) | 6244 | 689 |
| D40(Potholes) | 2316 | 248 |