Title: Enhanced licence plate detection using YOLO framework in challenging environments

Authors: Sahil Khokhar; Deepak Kedia

Addresses: Electronics and Communication Department, GJUS&T, Hisar – 125001, India ' Electronics and Communication Department, GJUS&T, Hisar – 125001, India

Abstract: The need for monitoring and controlling traffic for applications such as toll collection, parking, and law enforcement has grown significantly in the last few years. ALPR systems are accomplishing the monitoring of vehicles on a massive scale. The ALPR systems have been a research topic for many years, yet the ground deployment has yet to catch up. The primary reason for this issue has been the system's poor efficiency in real-world scenarios compared to the lab testing conditions. The focus of this paper has been on the license plate detection part of the ALPR system. The deep learning-based YOLO frameworks have been employed to detect license plates. The effect of using different datasets for training the network and the efficiency of various versions of the YOLO framework has also been tested in diverse conditions such as low-light low-contrast environments and partial or obstructed plates. The YOLOv7 algorithm achieved an F-score of 98.62% on the AOLP dataset with an average processing time of 15.43 ms. The implemented techniques are accurate and fast enough for real-time applications such as toll collection, traffic monitoring, etc.

Keywords: automatic license plate recognition; ALPR; object detection; deep learning; machine learning; computer vision; intelligent transportation system.

DOI: 10.1504/IJCVR.2025.149827

International Journal of Computational Vision and Robotics, 2025 Vol.15 No.6, pp.739 - 755

Received: 13 Oct 2022
Accepted: 15 Nov 2023
Published online: 14 Nov 2025 *

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