Presents an embedded LPR system with lightweight CNNs achieving 93.6% mAP detection and 87.88% recognition accuracy on the new SL-LPR dataset, running at 11.5 FPS on Xilinx Kria KV260 after Brevitas quantization and FINN FPGA acceleration.
LPRNet: License Plate Recognition via Deep Neural Networks
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abstract
This paper proposes LPRNet - end-to-end method for Automatic License Plate Recognition without preliminary character segmentation. Our approach is inspired by recent breakthroughs in Deep Neural Networks, and works in real-time with recognition accuracy up to 95% for Chinese license plates: 3 ms/plate on nVIDIA GeForce GTX 1080 and 1.3 ms/plate on Intel Core i7-6700K CPU. LPRNet consists of the lightweight Convolutional Neural Network, so it can be trained in end-to-end way. To the best of our knowledge, LPRNet is the first real-time License Plate Recognition system that does not use RNNs. As a result, the LPRNet algorithm may be used to create embedded solutions for LPR that feature high level accuracy even on challenging Chinese license plates.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
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citing papers explorer
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An Embedded Real-Time License Plate Recognition System for Complex Traffic Scenes
Presents an embedded LPR system with lightweight CNNs achieving 93.6% mAP detection and 87.88% recognition accuracy on the new SL-LPR dataset, running at 11.5 FPS on Xilinx Kria KV260 after Brevitas quantization and FINN FPGA acceleration.
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AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime
AIPC uses AI agents to automate PyTorch-to-QNN/SNPE deployment, completing it in 7-20 minutes for regular vision models at low API cost.