A Real-time LPR Deployment Scheme on Edge Devices via INT8 Quantization and TensorRT Optimization
DOI:
https://doi.org/10.65455/nygma907关键词:
License Plate Recognition (LPR), YOLOV5, Edge Computing, INT8 Quantization摘要
Real-time license plate recognition (LPR) on edge devices is heavily constrained by hardware limits. This study focuses on the efficient deployment of a real-time Chinese license plate recognition system, which plays a crucial role in intelligent traffic management and automated toll collection. Despite substantial progress in artificial intelligence and deep learning algorithms, achieving real-time performance remains a challenge because of the limited computational resources of practical hardware platforms. To overcome this limitation, a two-stage model optimization approach is proposed and integrated with NVIDIA deployment toolkits to accelerate inference with only marginal accuracy loss. The proposed work delivers a high-performance license plate recognition system and demonstrates that hardware-aware model optimization is essential for achieving efficient and practical real-time deployment.
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