CarNex: Real-Time License Plate Recognition
GPU-accelerated license plate detection and recognition system processing multiple video streams in real-time using NVIDIA DeepStream and YOLOv8.
The Challenge
Our client needed a high-performance license plate recognition system capable of processing multiple video streams simultaneously. Traditional OCR-based approaches couldn't keep up with the throughput and accuracy requirements for real-time intelligent transportation applications.
Our Approach
We engineered a GPU-accelerated pipeline built on NVIDIA's DeepStream SDK, combining state-of-the-art object detection with optimized inference.
Detection Pipeline
YOLOv8 was selected for license plate detection due to its exceptional balance of speed and accuracy. We trained custom models on diverse plate datasets to ensure robust performance across varying conditions — lighting, angles, weather, and plate formats.
Optimized Inference
We leveraged TensorRT for neural network optimization, converting models to INT8 precision for maximum throughput without sacrificing detection accuracy. CUDA kernels were used for custom post-processing operations directly on the GPU.
OCR Integration
Advanced OCR techniques were integrated downstream of the detection pipeline, with character-level recognition handling partial occlusions and non-standard plate formats.
Technical Stack
- Hardware: NVIDIA Jetson platform
- Frameworks: DeepStream SDK, TensorRT, CUDA
- Detection: YOLOv8 (custom-trained)
- Languages: C++, Python
- Integration: Real-time database for plate records
Results
- Exceptional performance processing multiple simultaneous video streams
- High-accuracy plate detection and character recognition
- Minimal latency suitable for real-time enforcement
- Robust platform extensible to additional transportation analytics