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--- |
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license: mit |
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tags: |
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- vision |
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- robotics |
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- drone-navigation |
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- vit |
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--- |
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# autonomous_drone_nav_vision |
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## Overview |
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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. |
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## Model Architecture |
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The model utilizes a **Vision Transformer (ViT-Base)** backbone: |
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- **Patch Extraction**: Images are divided into $16 \times 16$ fixed-size patches. |
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- **Position Embeddings**: Learnable spatial embeddings are added to the patch sequence to retain structural context. |
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- **Attention Mechanism**: Global self-attention allows the model to correlate distant visual cues, such as horizon lines and ground markers. |
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## Intended Use |
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- **Obstacle Avoidance**: Integrated into flight control stacks for autonomous "sense and avoid" maneuvers. |
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- **Precision Landing**: Identifying designated markers or flat terrain for autonomous recovery. |
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- **Search and Rescue**: Preliminary screening of aerial footage to identify human-made structures or anomalies. |
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## Limitations |
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- **Low Light**: Performance degrades significantly in nighttime or heavy fog conditions without thermal input. |
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- **Motion Blur**: Rapid yaw movements at high speeds may cause misclassification due to pixel streaking. |
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- **Scale Invariance**: Small objects at extreme altitudes may be missed due to the fixed $224 \times 224$ input resolution. |
