Table 2.
Simplified comparison of object detection and segmentation models.
| Model | Key features | Computational complexity | Inference speed (FPS) | Application scenarios |
|---|---|---|---|---|
| YOLOv11-seg74 | Fast single-stage detection + segmentation | Low | 30–60 | Real-time landslide detection (UAV) |
| DETR75 | Transformer-based, no anchor boxes | High | 10–20 | Complex scene object detection |
| U-Net76 | Full-image segmentation, high precision | High | 5–10 | Landslide segmentation |
| Mask R-CNN76 | Two-stage detection, detailed masks | Very high | 2–7 | High-precision segmentation |
| DeepLabV3+77 | Dilated convolution for better segmentation | High | 8–15 | Large-scale land segmentation |
| SegFormer78 | Transformer-based, robust segmentation | High | 5–10 | Large-area segmentation |
| EfficientDet79 | Lightweight, efficient detection | Low | 20–30 | Mobile deployment, efficient |
| Swin Transformer80 | High-precision Transformer model | Extremely high | 5–8 | Complex landslide recognition |