vanrakshak-forest-yolov8n

🌲 VanRakshak YOLOv8n: Edge Aerial & Thermal Forest Drone Detector

VanRakshak YOLOv8n is a real-time object detection model fine-tuned for autonomous drone surveillance, anti-poaching patrol, wildfire detection, and wildlife conservation.

Part of the open-source VanRakshak Autonomous Drone Platform.

---

📊 Measured Benchmark Scores (Tesla T4 GPU)

Evaluated on 1,786 held-out validation aerial and thermal drone frames:

---

🏷️ Class List (6 Classes)

0: person        # Intruders, poachers, and hikers in RGB aerial & night thermal
1: vehicle       # Civilian cars, vans, and motorcycles on forest tracks
2: timber_truck  # Heavy timber transport and illegal logging flatbeds
3: fire          # Active wildfire flame fronts and spot fires
4: smoke         # Early wildfire smoke plumes and combustion clouds
5: elephant      # Wildlife (elephants and megafauna) for corridor monitoring

---

🚀 Quickstart & Inference

Python (Ultralytics)

from ultralytics import YOLO

# Load model directly
model = YOLO("best.pt")

# Run inference on drone aerial or thermal frame
results = model("drone_thermal_frame.jpg", conf=0.50)

# Visualize predictions
for r in results:
    r.show()

Edge ONNX Inference (Drone Companion Computer)

import onnxruntime as ort

session = ort.InferenceSession("best.onnx", providers=["CPUExecutionProvider"])
# Optimized for Raspberry Pi 5 and NVIDIA Jetson Orin Nano

---

🔗 Links & Resources

• 💻 GitHub Codebase: https://github.com/sanjeevafk/vanrakshak
• 📦 Training Dataset: sanjeevafk/vanrakshak-forest-aerial-thermal
• 📓 Colab Training Pipeline: vanrakshak_yolov8_training.ipynb

---

⚖️ License

Released under the Apache 2.0 License.