2/3/2025 | 3:30 PM - 5:30 PM | Grand Peninsula A

JIST-first ACCEPTED: Performance of Automatic License Plate Recognition Systems on Distorted Images

Author(s)

Nikola Plavac
Seyed Amirshahi
Marius Pedersen
Sophie Triantaphillidou

Abstract

Automatic License Plate Recognition (ALPR) systems are essential for various applications, including law enforcement, traffic management, and access control. However, their performance can be significantly affected by images distorted by adverse environmental conditions and the imaging pipeline. Three different ALPR systems are used to evaluate their robustness to different distortions using images from six well-known ALPR datasets. Two groups of distortions are the focus of the study: simulated weather conditions (rain, brightness, fog, frost, and snow), and modeled camera read noise in the simulated imaging pipeline. The findings indicate that certain weather distortions drastically reduce the accuracy of ALPR systems, with the accuracy of the systems approaching zero in some cases. Read noise also negatively impacts performance, even at minimal levels. The sensitivity to the introduced distortions varied between different models and datasets. The results underscore the need for robust ALPR system designs that can handle diverse and challenging capturing conditions.