---
license: mit
tags:
- vision
- robotics
- drone-navigation
- vit
---

# autonomous_drone_nav_vision

## Overview
A Vision Transformer (ViT) fine-tuned for tactical aerial navigation. This model enables Small Unmanned Aircraft Systems (sUAS) to classify environmental obstacles and identify safe landing zones in real-time using downward and forward-facing RGB cameras.



## Model Architecture
The model utilizes a **Vision Transformer (ViT-Base)** backbone:
- **Patch Extraction**: Images are divided into $16 \times 16$ fixed-size patches.
- **Position Embeddings**: Learnable spatial embeddings are added to the patch sequence to retain structural context.
- **Attention Mechanism**: Global self-attention allows the model to correlate distant visual cues, such as horizon lines and ground markers.

## Intended Use
- **Obstacle Avoidance**: Integrated into flight control stacks for autonomous "sense and avoid" maneuvers.
- **Precision Landing**: Identifying designated markers or flat terrain for autonomous recovery.
- **Search and Rescue**: Preliminary screening of aerial footage to identify human-made structures or anomalies.

## Limitations
- **Low Light**: Performance degrades significantly in nighttime or heavy fog conditions without thermal input.
- **Motion Blur**: Rapid yaw movements at high speeds may cause misclassification due to pixel streaking.
- **Scale Invariance**: Small objects at extreme altitudes may be missed due to the fixed $224 \times 224$ input resolution.