An Embedded Real-Time License Plate Recognition System for Complex Traffic Scenes

2026-06-26

AI Summary

This work addresses the challenge of low-cost, real-time license plate recognition in complex, unstructured traffic environments typical of developing countries by proposing a lightweight two-stage system tailored for embedded platforms. The approach leverages a compact convolutional neural network for both license plate detection and character recognition. Key contributions include the creation of SL-LPR, the first dataset specifically designed for such scenarios, and an efficient deployment on the Xilinx Kria KV260 FPGA using Brevitas for low-bit quantization and the FINN framework. Evaluated on the SL-LPR dataset, the system achieves a detection mAP of 93.6% and a character recognition accuracy of 87.88%, delivering end-to-end inference at 11.5 frames per second.

Abstract

Vehicle license plate recognition is an integral component of intelligent transportation systems. In this work, we present an embedded real-time license plate recognition system customized for developing countries. We address the challenge of handling complex, unstructured traffic scenes with diverse vehicle types while implementing the system on an embedded platform for low-cost deployment. Our method consists of license plate detection on a multi-vehicle image, followed by character recognition on the detected license plates. Both steps use lightweight convolutional neural networks to balance accuracy and efficiency. We also introduce the SL-LPR dataset of Sri Lankan road images, which contains a variety of vehicle types and traffic conditions typically seen in developing countries. On this dataset, the license plate detection and character recognition models achieved 93.6% mAP and 87.88% accuracy, respectively, and were competitive against larger models on several public datasets. To achieve real-time performance in a resource-constrained embedded environment, we applied low-bitwidth quantization using the Brevitas library and implemented FPGA acceleration for the models using the FINN framework. The end-to-end system can operate at 11.5~FPS when implemented on the Xilinx Kria KV260 platform. These results demonstrate that our system is effective for real-time license plate recognition on an embedded device, even in complex traffic scenarios. The SL-LPR dataset is available for research use at: https://github.com/sl-lpr-uom/SL-LPR.git.

Problem

• license plate recognition
• embedded system
• real-time processing
• complex traffic scenes
• developing countries

Innovation

• lightweight CNN
• embedded real-time system
• low-bitwidth quantization
• FPGA acceleration
• license plate recognition