---
license: mit
tags:
- ultralytics
- yolo
- yolo11
- object-detection
- military-vehicle-detection
- convoy-detection
- visdrone
- aerial-detection
- OmniSense
- MediaSense
- DeSense
- ConnectiviaLabs
datasets:
- visdrone
- military-vehicle-recognition
pipeline_tag: object-detection
library_name: ultralytics
---

# MilitaryConvoy-YOLO11L — Military Vehicle & Convoy Detection

Part of **OmniSense / MediaSense / DeSense** — Connectivia Labs' defense AI platform.

## Performance
| Metric | Value |
|--------|-------|
| **mAP@0.5** | **50.0%** |
| **mAP@0.5:0.95** | **30.4%** |
| Precision | 57.0% |
| Recall | 47.7% |
| Architecture | YOLO11L |
| Input size | 640×640 |
| Training date | 2026-04-05 |

## Classes (15 total)
**Aerial/civilian (VisDrone):** `pedestrian` · `person` · `bicycle` · `car` · `van` · `truck` · `tricycle` · `awning-tricycle` · `bus` · `motor`

**Military (DeSense):** `military-vehicle` · `tank` · `apc` · `afv` · `artillery`

## Training
- Base: YOLO11L (COCO pretrained)
- Dataset: VisDrone2019-DET + MilitaryVehicleRecognition v7 (~9,300 images merged)
- Epochs: 75 | Batch: 32 | Imgsz: 640 | Optimizer: AdamW + cosine LR
- Augmentation: mosaic, mixup, copy-paste, flips, scale

## Usage
```python
from ultralytics import YOLO
model = YOLO('MuayThaiLegz/MilitaryConvoy-YOLO11L')
results = model('aerial_frame.jpg')

# Filter military classes only (indices 10-14)
mil = [b for b in results[0].boxes if int(b.cls) >= 10]
print(f'Military vehicles: {len(mil)}')
```

## DeSense Pipeline
```
Camera/Satellite → MilitaryConvoy-YOLO11L → ByteTrack
→ DBSCAN convoy clustering → Stone Soup fusion → Geofence alert
```

> **OmniSense** · Pillars: MediaSense · DeSense · Connectivia Labs 🔒