Model Card for rebotnix/rb_vehicle
🚗 Vehicle Detection in Aerial and Drone Imagery – Trained by KINEVA, Built by REBOTNIX, Germany Current State: in production and re-training.
This object detection model identifies vehicles (cars, trucks, buses, and more) in high-resolution aerial and drone imagery. It has been trained on a curated dataset featuring a wide range of vehicle types, altitudes, environments, and conditions. The model is ideal for use in urban planning, traffic analysis, infrastructure monitoring and smart city projects.
Developed and maintained by REBOTNIX, Germany, https://rebotnix.com
About KINEVA
KINEVA® is an automated training platform based on the MCP Agent system. It regularly delivers new visual computing models, all developed entirely from scratch. This approach enables the creation of customized models tailored to specific client requirements, which can be retrained and re-released as needed. The platform is particularly suited for applications that demand flexibility, adaptability, and technological precision—such as industrial image processing, smart city analytics, or automated object detection.
KINEVA is continuously evolving to meet the growing demands in the fields of artificial intelligence and machine vision. https://rebotnix.com/en/kineva
🚗 Example Predictions
| Input Image | Detection Result |
|---|---|
 |
 |
 |
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| (More example visualizations coming soon) |
Model Details
- Architecture: RF-DETR (custom training head with optimized anchor boxes)
- Task: Object Detection (Vehicle class)
- Trained on: REBOTNIX Aerial Vehicle Dataset (proprietary)
- Format: PyTorch
.pth+ ONNX and trt export available on request - Backbone: EfficientNet B3 (adapted)
- Training Framework: PyTorch + RF-DETR + custom augmentation
Chart
Dataset
The training dataset consists of high-resolution aerial imagery collected from:
- Open-source satellite archives
- Licensed drone operations over urban and rural environments
- Custom annotated bounding boxes by REBOTNIX team
The model was trained to be robust across:
- Different vehicle sizes (motorcycles, passenger cars, buses, trucks)
- Environmental variations (urban centers, highways, industrial zones)
- Occlusions (overpasses, trees, buildings)
- Diverse lighting conditions (clear, cloudy)
Intended Use
| ✅ Intended Use | ❌ Not Intended Use |
|---|---|
| Traffic monitoring and analytics | Autonomous vehicle navigation |
| Urban infrastructure analysis | Real-time military target acquisition |
| Smart city surveillance planning | Nighttime thermal vehicle detection (unsupported) |
Limitations
- Performance can degrade under extreme occlusion (heavy shadows, under bridges)
- Not optimized for night-time or infrared imagery
- Small vehicles (e.g., motorcycles in crowded scenes) may occasionally be missed
Usage Example
import supervision as sv
from PIL import Image
from rfdetr import RFDETRBase
model_path= "./rb_vehicle.pth"
CLASS_NAMES = ["vehicle"]
model = RFDETRBase(pretrain_weights=model_path,num_classes=len(CLASS_NAMES))
image_path = "./example_vehicle1.jpg"
image = Image.open(image_path)
detections = model.predict(image, threshold=0.15)
labels = [
f"{CLASS_NAMES[class_id]} {confidence:.2f}"
for class_id, confidence
in zip(detections.class_id, detections.confidence)
]
print(labels)
annotated_image = image.copy()
annotated_image = sv.BoxAnnotator().annotate(annotated_image, detections)
annotated_image = sv.LabelAnnotator().annotate(annotated_image, detections, labels)
annotated_image.save("./output_1.jpg")
Contact
📫 For commercial use or re-training this model support, or dataset access, contact:
REBOTNIX
✉️ Email: communicate@rebotnix.com
🌐 Website: https://rebotnix.com
License
This model is released under CC-BY-NC-SA unless otherwise noted. For commercial licensing, please reach out to the contact email.