Research on infrared image target detection technology based on deep learning
, , e
17 mar 2025
INFORMAZIONI SU QUESTO ARTICOLO
Pubblicato online: 17 mar 2025
Ricevuto: 19 ott 2024
Accettato: 03 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0325
Parole chiave
© 2025 Jing Gao et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1.
Figure 2.
Figure 3.
Figure 4.
Figure 5.
The comparison results of the lightweight algorithm model experiment
| Model | Parameter quantity(M) | Model size(MB) | Precision | Recall | mAP | FLOPs(G) |
|---|---|---|---|---|---|---|
| YOLOv3-tiny | 9.35 | 18.08 | 0.800 | 0.620 | 0.706 | 13.58 |
| YOLOv5 | 7.69 | 14.37 | 0.827 | 0.728 | 0.814 | 16.48 |
| YOLOv5-ghost | 4.36 | 8.58 | 0.832 | 0.728 | 0.811 | 8.78 |
| YOLOv5-repvgg | 7.69 | 15.48 | 0.845 | 0.778 | 0.839 | 16.48 |
| YOLOv7-tiny | 6.69 | 12.98 | 0.813 | 0.754 | 0.826 | 13.68 |
| Ours | 9.60 | 16.48 | 0.857 | 0.811 | 0.862 | 22.68 |
The parameter modulus model compares the results
| Model | Parameter quantity(M) | Model size(MB) | Precision | Recall | mAP | FLOPs(G) |
|---|---|---|---|---|---|---|
| YOLOv5m | 21.03 | 42.37 | 0.831 | 0.772 | 0.835 | 48.58 |
| YOLOv6s | 17.37 | 36.47 | 0.833 | 0.786 | 0.840 | 44.88 |
| YOLOv7 | 36.66 | 74.97 | 0.861 | 0.814 | 0.865 | 103.88 |
| YOLOv8s | 11.29 | 22.67 | 0.835 | 0.775 | 0.841 | 29.08 |
| Ours | 9.60 | 16.48 | 0.857 | 0.811 | 0.862 | 22.68 |
The model deployment scheme compares the results
| Index | Accuracy | Running time(ms) | Memory footprint(KB) | CPSA(%) | Parameter quantity |
|---|---|---|---|---|---|
| Scheme | |||||
| Torch | 50.43 | 44.946 | 2815645 | 80.45 | 45 |
| Darknet | 55.91 | 102.757 | 2795642 | 35.42 | 95 |
| TensorRT | 53.32 | 52.854 | 2809325 | 68.45 | 51 |
| Ours | 54.87 | 43.150 | 2584360 | 50.21 | 40 |