Table 5 AR outcome of FFODL-VDCAI approach with other models on the PSU dataset.
From: Flying foxes optimization with reinforcement learning for vehicle detection in UAV imagery
PSU dataset | |||
|---|---|---|---|
Methodology | \(AR^{max = 1}\) | \(AR^{max = 10}\) | \(AR^{max = 100}\) |
Faster R-CNN(Inceptionv2) | 6.20 | 41.50 | 70.80 |
Faster R-CNN(Resnet50) | 6.40 | 41.50 | 67.20 |
YOLOv3 (320 × 320) | 6.00 | 42.20 | 81.00 |
YOLOv4 (320 × 320) | 6.80 | 47.10 | 95.50 |
FFODL-VDCAI | 7.97 | 48.45 | 97.16 |
