MicroCharNet: An Ultra-Lightweight Model for License Plate Character Detection (arXiv 2607.11830)

Forum topic · 小凯 · 2026-07-15

Summary

MicroCharNet (arXiv 2607.11830) is an ultra-lightweight deep learning model designed for license plate character detection in intelligent transportation systems. Developed by Huy Che, Dinh-Duy Phan, and Duc-Lung Vu, the architecture combines a compact backbone built from C2f blocks with CoordAtt attention modules to enhance feature extraction while preserving spatial information. A lightweight C3k2-based neck fuses multi-level features, followed by a single-stage anchor-free detection head for end-to-end prediction. On the UFPR-ALPR dataset, MicroCharNet achieves competitive detection accuracy with only 0.08M parameters and 0.096 GFLOPs, outperforming several recent YOLO baselines. Hardware-level evaluations confirm real-time deployment efficiency on edge devices, showing that carefully designed ultra-lightweight architectures can effectively balance accuracy and computational efficiency for resource-constrained ALPR applications.

Paper Overview

Research Area: Computer Vision Authors: Huy Che, Dinh-Duy Phan, Duc-Lung Vu Published: 2026-07-13 arXiv: 2607.11830

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.

The authors propose MicroCharNet, an ultra-lightweight model specifically designed for license plate character detection. Key architectural elements:

A compact backbone composed of C2f blocks, integrated with the CoordAtt module to enhance feature extraction while preserving spatial information.
A lightweight C3k2-based neck that fuses multi-level features.
A single-stage anchor-free detection head enabling end-to-end prediction.

Results

On the UFPR-ALPR dataset, MicroCharNet achieves competitive detection accuracy with only 0.08M parameters and 0.096 GFLOPs, while outperforming several recent YOLO baselines.
Hardware-level evaluations confirm its efficiency for real-time deployment on edge devices.

These results demonstrate that carefully designed ultra-lightweight architectures can effectively balance accuracy and efficiency in license plate character detection.

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*Auto-collected on 2026-07-15*