Ultra-Lightweight Automatic License Plate Recognition System for Microcontrollers: A Cost-Effective and Energy-Efficient Solution
Yan Tang, Shaoming Zhang, Weijia Liu, Zhong Wang, Jianmei Wang
Abstract
In the realm of intelligent transportation systems, Automatic License Plate Recognition (ALPR) is integral, yet is often hindered by heavy computational loads and massive memory footprints. Existing methods either enhance recognition accuracy through complex models or minimize resource requirements by utilizing low-performance models and low-resolution inputs, both of which pose challenges in real-world applications. To address these issues, this paper introduces a novel ultra-lightweight ALPR system specifically designed for deployment on microcontrollers, offering a cost-effective and energy-efficient solution for large-scale applications. We simplify the process by decoupling ALPR into three sub-tasks: detection, localization, and recognition. For each sub-task, specific improvement strategies such as Residual Log-likelihood Estimation (RLE)-based keypoint localization, adaptive data augmentation, and multi-voting recognition mechanism are designed. Our proposed system naturally mitigates the issues of large perspective transformations frequently encountered in real-world settings. As a supplementary contribution, we introduce and release the TJLP dataset. Practical application and superior performance of our method have been demonstrated on resource-limited devices like RV1106 (ARM Cortex-A7 with a 0.5 TOPS NPU), achieving an inference speed of 22 FPS with a power consumption of 629 mW. Notably, our fully optimized model exhibits a recognition accuracy of 99.23% in real-world environments.