Research on infrared image target detection technology based on deep learning
, , oraz
17 mar 2025
O artykule
Data publikacji: 17 mar 2025
Otrzymano: 19 paź 2024
Przyjęty: 03 lut 2025
DOI: https://doi.org/10.2478/amns-2025-0325
Słowa kluczowe
© 2025 Jing Gao et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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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 |