| --- |
| 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 🔒 |
|
|
