MicroCharNet: Less is More for License Plate Character Detection

2026-07-13

AI Summary

This work proposes MicroCharNet, an ultra-lightweight model designed for efficient license plate character detection on resource-constrained devices. The architecture employs a compact backbone built with C2f modules, enhanced by CoordAtt attention mechanisms, a lightweight C3k2 neck, and a single-layer anchor-free detection head to enable end-to-end efficient prediction. With only 0.08 million parameters and 0.096 GFLOPs, MicroCharNet outperforms multiple YOLO baselines on the UFPR-ALPR dataset, achieving high accuracy while maintaining minimal computational overhead. The model’s real-time deployment capability on edge devices demonstrates that an extremely streamlined architecture can effectively balance efficiency and performance in practical applications.

Abstract

License plate character detection is a crucial component of intelligent transportation systems, where high accuracy and computational efficiency are required for real-time deployment. Although recent deep learning-based methods have substantially improved detection performance, many high-accuracy models rely on large-scale architectures that incur substantial computational overhead, limiting their applicability to resource-constrained devices. In this paper, we propose MicroCharNet, an ultra-lightweight model specifically designed for license plate character detection. The proposed architecture employs a compact backbone composed of C2f blocks, integrated with CoordAtt module to enhance feature extraction while preserving spatial information. A lightweight C3k2-based neck fuses multi-level features, followed by a single-level anchor-free detection head that enables end-to-end prediction. Experiments conducted on the UFPR-ALPR dataset demonstrate that MicroCharNet achieves competitive detection accuracy with only 0.08M parameters and 0.096 GFLOPs, while outperforming several recent YOLO-based baselines. Hardware-level evaluations further confirm its efficiency for real-time deployment on edge devices. These results indicate that carefully designed ultra-lightweight architectures can effectively balance accuracy and efficiency in license plate character detection. The source code is available at https://github.com/chequanghuy/MicroCharNet.

Problem

• license plate character detection
• computational efficiency
• resource-constrained devices
• real-time deployment
• ultra-lightweight model

Innovation

• ultra-lightweight
• license plate character detection
• anchor-free
• CoordAtt
• edge deployment