Table 7.
Accuracy comparison of the proposed approach with SOTA vehicle detection models.
| Methods | Accuracy % | |||
|---|---|---|---|---|
| VEDAI | SRTID | VAID | UAVDT | |
| AVD NET (Mandal et al., 2020) | 51.95 | 62.10 | 60.75 | 58.20 |
| YOLOv5 (Hou S. et al., 2023) | 75.54 | 73.20 | 74.30 | 72.10 |
| Haar-like features (Nguyen and Tran, 2018) | 77.0 | 65.00 | 64.50 | 62.00 |
| D2Det (Cao et al., 2020) | 73.40 | 56.92 | 68.10 | 64.30 |
| R-FCN (Zhang, 2020) | 68.90 | 73.0 | 69.80 | 70.20 |
| SSD (Smith and Johnson, 2020) | 71.00 | 70.30 | 81.0 | 74.50 |
| R-FCN (Kim and Park, 2021) | 72.50 | 70.90 | 75.0 | 73.20 |
| NDFT (Cao et al., 2020) | 63.50 | 62.80 | 64.00 | 52.03 |
| YOLOv6 (Kumar and Singh, 2023) | 74.07 | 71.20 | 72.80 | 74.07 |
| YOLOv7 (Patel and Reddy, 2023) | 76.80 | 74.50 | 75.60 | 72.0 |
| Proposed method | 79.4 | 77.7 | 83.1 | 77.2 |