License Plate Detection via Information Maximization

Younkwan Lee,Yeongmin Ko,Jihyo Jeon,Moongu Jeon,Witold Pedrycz

doi:10.1109/tits.2021.3135015

Abstract

License plate (LP) detection in the wild remains challenging due to the diversity of environmental conditions. Nevertheless, prior solutions have focused on controlled environments, such as when LP images frequently emerge as from an approximately frontal viewpoint and without scene text which might be mistaken for an LP. However, even for state-of-the-art object detectors, their detection performance is not satisfactory for real-world environments, suffering from various types of degradation. To solve these problems, we propose a novel end-to-end framework for robust LP detection, designed for such challenging settings. Our contribution is threefold: (1) A novel information-theoretic learning that takes advantage of a shared encoder, an LP detector and a scene text detector (excluding LP) simultaneously; (2) Localization refinement for generalizing the bounding box regression network to complement ambiguous detection results; (3) a large-scale, comprehensive dataset, LPST-110K, representing real-world unconstrained scenes including scene text annotations. Computational tests show that the proposed model outperforms other state-of-the-art methods on a variety of challenging datasets.

Highlights

O BJECT detection research has attracted great interest in recent years, with models being applied widely in many traffic-related applications [1]–[6]
By evaluating stateof-the-art detection models on LPST-110K, we demonstrate the accuracy improvement of our proposed model compared with other approaches
Comparisons With State-of-the-Art Methods For the AOLP, PKU, UFPR, CCPD and LPST-110K, our proposed method can significantly improve the performance of detection, including challenging real-world images as shown in Fig 5 and 7

Summary

Introduction

O BJECT detection research has attracted great interest in recent years, with models being applied widely in many traffic-related applications [1]–[6].

Objectives

Methods

Results

Conclusion