ALPR Under 100ms with Jetson Orin Nano Super

Jetson Orin Nano Super: New Era in Edge AI

NVIDIA's Jetson Orin Nano Super platform is revolutionizing the edge computing world. 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 a powerful platform equipped with 1024 CUDA cores and 32 Tensor cores. Our VEBZE ALPR model utilizes this hardware as follows:

• Inference Time: 85-95ms range (1920x1080 resolution)
• Batch Processing: 4 frames can be processed simultaneously
• FP16 Precision: 2x speed increase without accuracy loss
• TensorRT Optimization: Model size reduced 40%, speed increased 60%

Optimization Process with TensorRT

When optimizing the VEBZE ALPR model for Jetson Orin Nano Super, we follow these steps:

1. Model Quantization: FP32 to FP16 conversion, INT8 calibration
2. Layer Fusion: Merging unnecessary layers
3. Kernel Auto-Tuning: Jetson hardware-specific optimization
4. Dynamic Batch Size: Adaptive processing based on load

Edge Computing Advantages

Our ALPR system running on Jetson Orin Nano Super offers critical advantages over cloud-based solutions:

Use Cases

With inference time under 100ms, these applications become possible:

• Speed Barriers: Plates of high-speed vehicles read instantly
• Multi-Entry Parking: Single Jetson device manages 4 entrances
• Traffic Violation Detection: Real-time plate reading and recording
• Smart City Projects: Low-cost, high-performance solution

Conclusion

Jetson Orin Nano Super is an ideal edge computing platform for ALPR applications. With VEBZE's optimized AI model, we achieve inference time under 100ms, 99%+ accuracy, and low power consumption. The future of edge AI starts here.