Optimisation of highway vehicle occlusion recognition based on attention and multitasking approach
17. März 2025
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Online veröffentlicht: 17. März 2025
Eingereicht: 18. Okt. 2024
Akzeptiert: 12. Feb. 2025
DOI: https://doi.org/10.2478/amns-2025-0180
Schlüsselwörter
© 2025 Shifeng Feng, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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Comparison of different methods for detecting occluded vehicles
| Data set | Model | P/% | R/% | F1/% | MSE | MAE |
|---|---|---|---|---|---|---|
| COCO | Mask R-CNN | 79.33 | 82.29 | 80.81 | 0.04 | 0.06 |
| Faster R-CNN | 84.69 | 84.17 | 84.43 | 0.02 | 0.04 | |
| YOLOv7 | 89.75 | 88.74 | 89.32 | 0.02 | 0.02 | |
| Our method | 93.28 | 92.47 | 92.82 | 0.01 | 0.02 | |
| ApolloScape | Mask R-CNN | 84.25 | 86.54 | 85.41 | 0.03 | 0.06 |
| Faster R-CNN | 86.69 | 88.15 | 87.36 | 0.03 | 0.05 | |
| YOLOv7 | 89.51 | 90.33 | 89.51 | 0.03 | 0.04 | |
| Our method | 92.23 | 93.82 | 92.62 | 0.01 | 0.02 |
Comparison of model detection results under different occlusion conditions
| Occlusion rate | Method | Color precision/% | Vehicle type precision/% | Detection time/s |
|---|---|---|---|---|
| 20% | Mask R-CNN | 79.08 | 87.22 | 1.04 |
| Faster R-CNN | 76.33 | 72.23 | 1.46 | |
| YOLOv7 | 82.81 | 73.52 | 1.22 | |
| Our method | 91.74 | 93.49 | 0.29 | |
| 40% | Mask R-CNN | 80.13 | 79.45 | 0.93 |
| Faster R-CNN | 75.62 | 86.51 | 0.77 | |
| YOLOv7 | 82.77 | 89.52 | 0.61 | |
| Our method | 90.06 | 89.06 | 0.44 | |
| 60% | Mask R-CNN | 80.45 | 80.94 | 0.55 |
| Faster R-CNN | 83.92 | 78.25 | 1.94 | |
| YOLOv7 | 76.76 | 83.49 | 0.74 | |
| Our method | 94.56 | 88.67 | 0.21 | |
| 80% | Mask R-CNN | 78.77 | 81.47 | 1.28 |
| Faster R-CNN | 82.36 | 84.22 | 1.18 | |
| YOLOv7 | 84.58 | 87.11 | 1.92 | |
| Our method | 92.52 | 88.16 | 0.53 |