SAM vs Faster R-CNN: A Practical Comparison for Aerial Imagery

January 3, 2026 • 10 min read

When building object detection systems for aerial imagery, you face a fundamental architectural choice: convolutional detectors like Faster R-CNN or transformer-based segmentation models like SAM. Both can detect objects in drone orthomosaics, but they make different tradeoffs in speed, accuracy, and deployment complexity. This post compares the two approaches using empirical results from pot detection in agricultural imagery.

Architecture Differences

Faster R-CNN uses a ResNet50-FPN backbone—a convolutional architecture with 2048 channels in its deepest layer. The Feature Pyramid Network merges features at multiple scales, making it effective for objects of varying sizes. The entire model is end-to-end trainable: feed in an image, get back bounding boxes with confidence scores.

SAM (Segment Anything Model) uses a ViT-B image encoder—a vision transformer with 1024-dimensional embeddings. Unlike Faster R-CNN, SAM requires prompts: bounding boxes, points, or text to guide segmentation. It outputs pixel-precise masks rather than bounding boxes.

# Architecture summary
Faster R-CNN:  Image → ResNet50-FPN (2048 ch) → Boxes + Scores
SAM:           Image → ViT-B (1024 ch) → Prompt → Masks

Fine-Tuning Approaches

The models require different fine-tuning strategies. Faster R-CNN benefits from full model training—all 41 million parameters adapt to the new domain. For aerial imagery with distinctive features (overhead views, consistent lighting, specific object types), this comprehensive adaptation pays off.

SAM's ViT encoder is so powerful that freezing it works well. We fine-tune only the lightweight mask decoder (~4 million parameters), keeping the encoder's learned representations intact. This parameter-efficient approach trains faster and requires less data.

# SAM decoder-only fine-tuning
for param in sam_model.image_encoder.parameters():
    param.requires_grad = False  # Freeze encoder

for param in sam_model.mask_decoder.parameters():
    param.requires_grad = True   # Train decoder only

# ~4M trainable vs 93M total parameters

The learning rate matters more for SAM. We use 1e-4 (Adam optimizer) versus 5e-3 (SGD) for Faster R-CNN. The transformer-based decoder is more sensitive to large updates.

Speed Comparison

For autonomous detection, Faster R-CNN is faster. On a 1024x1024 tile with an RTX 3080:

Faster R-CNN: ~50ms per tile (detection only)
SAM:          ~150ms per tile (with bbox prompts)

SAM requires two passes: one to encode the image, another to decode masks from prompts. If you're using SAM with Faster R-CNN boxes as prompts (a hybrid approach), total inference time is ~200ms per tile.

For large orthomosaics with hundreds of tiles, this difference compounds. A 15,000x12,000 pixel image at 1024x1024 tiles with 128px overlap requires ~200 tiles. Faster R-CNN processes this in ~10 seconds; SAM with prompts takes ~40 seconds.

Accuracy and Output Quality

SAM produces pixel-precise segmentation masks; Faster R-CNN produces bounding boxes. For circular objects like plant pots, the difference is significant—SAM captures the actual shape while boxes include background pixels.

However, for navigation and counting tasks, bounding boxes often suffice. The additional precision of segmentation masks only matters when you need exact object boundaries (quality inspection, volume estimation, occlusion handling).

In our pot detection experiments, Faster R-CNN achieves comparable detection rates to SAM when using IoU metrics that account for the box-vs-mask difference. Mean IoU between Faster R-CNN boxes and SAM masks on the same detections is ~0.7—the box is a reasonable approximation.

Feature Representations

Comparing internal representations reveals architectural differences. ResNet features are translation equivariant—the same filter activates wherever a pattern appears. ViT features incorporate global context from the start through self-attention.

PCA visualization of backbone features shows this clearly. ResNet layer4 features (2048 channels) cluster spatially—nearby pixels have similar representations. ViT features (1024 channels) show more semantic grouping, with similar objects having similar representations regardless of position.

For interpretability research, both representations are useful but reveal different aspects of what the model has learned.

When to Use Each

Use Faster R-CNN when:

You need autonomous detection without prompts
Bounding boxes are sufficient for your task
Real-time or near-real-time performance is critical
Deploying on edge devices with limited memory
Training data is abundant

Use SAM when:

Pixel-precise masks are necessary
Training data is limited (parameter-efficient fine-tuning)
Interactive refinement is valuable (human-in-loop workflows)
Multi-task learning is planned (health assessment, size estimation)
Transfer to new object types with minimal retraining

Use a hybrid approach when:

Speed matters but you also need precise masks
Faster R-CNN handles detection, SAM refines boundaries
Best of both: fast detection + precise segmentation

Practical Recommendations

For most aerial object detection tasks, start with Faster R-CNN. It's simpler to deploy, faster at inference, and produces outputs that integrate easily with downstream systems. The training pipeline is well-documented and the model is robust.

Consider SAM when mask precision matters or when you're building interactive tools where users refine detections. The decoder-only fine-tuning approach makes SAM surprisingly data-efficient—a few hundred annotated examples can produce strong results.

For research into what models learn (mechanistic interpretability), both architectures offer valuable perspectives. ResNet's hierarchical features are easier to interpret; ViT's attention patterns reveal different inductive biases.