ALPR Under 100ms with Jetson Orin Nano Super
~2 min
Jetson Orin Nano Super: New Era in Edge AI
NVIDIA's Jetson Orin Nano Super platform is revolutionizing edge computing. With VEBZE's optimized ALPR (Automatic License Plate Recognition) model, we achieve inference times under 100 milliseconds.
Technical Specifications and Performance
Jetson Orin Nano Super is equipped with 1024 CUDA cores and 32 Tensor cores. Our VEBZE ALPR model leverages this hardware as follows:
- Inference Time: 85–95ms (1920×1080 resolution)
- Batch Processing: 4 frames processed simultaneously
- FP16 Precision: 2× speed increase without accuracy loss
- TensorRT Optimization: Model size reduced 40%, speed increased 60%
Real-World Performance
| Metric | Value |
|---|---|
| Test scenario | 1920×1080, 4-channel video stream |
| Average inference time | 92ms |
| Peak performance | 43 FPS (single camera) |
| Accuracy rate | 99.2% (Turkish plates) |
Optimization Process with TensorRT
- Model Quantization: FP32 → FP16 conversion, INT8 calibration
- Layer Fusion: Merging redundant layers
- Kernel Auto-Tuning: Jetson-specific hardware optimization
- Dynamic Batch Size: Adaptive processing based on load
Edge Computing Advantages
| Advantage | Detail |
|---|---|
| ⚡ Low Latency | Sub-100ms response, no network round-trip |
| 🔒 Data Security | Images never leave the device — GDPR/KVKK compliant |
| 💰 Cost Savings | No cloud API fees, one-time hardware investment |
| 🌐 Offline Operation | Continuous service without internet connectivity |
Use Cases
- Speed Barriers: Plates of fast-moving vehicles read instantly
- Multi-Entry Parking: Single Jetson device manages 4 entrances
- Traffic Violation Detection: Real-time plate reading and logging
- Smart City Projects: Low-cost, high-performance deployment
Conclusion
Jetson Orin Nano Super is the ideal edge computing platform for ALPR. With VEBZE's optimized AI model you get sub-100ms inference, 99%+ accuracy, and low power consumption.