Integrating GAN-Based Image Enhancement with YOLOv5 Object Detection for Accurate Vehicle Number Plate Analysis

Main Article Content

M.Bhavsingh
K Venkatesh Sharma
Mustafa Abdulkareem Salman Al-Nuaimi

Abstract

In this research, we propose a novel approach for the analysis of vehicle number plates, crucial for applications ranging from transportation management to law enforcement. Our methodology leverages the power of Generative Adversarial Networks (GANs) and YOLOv5, a state-of-the-art object detection model. We start by curating a diverse dataset of vehicle number plate images, encompassing variations in lighting, orientation, and image quality. We employ GANs to preprocess the dataset, enhancing image quality, reducing noise, and expanding dataset size, thus ensuring robust performance under real-world conditions. Subsequently, YOLOv5 is employed for rapid and accurate object detection within the preprocessed images, forming the core of our analysis pipeline. Through comprehensive experiments on various datasets and real-world scenarios, our approach consistently outperforms traditional methods in vehicle number plate detection and recognition tasks, demonstrating its adaptability to varying environmental conditions, including day and night settings. This research contributes significantly to the automated license plate recognition (ALPR) and image analysis field by introducing an efficient system that combines GANs' generative capabilities with YOLOv5's precision, promising advancements in transportation management, security surveillance, and law enforcement applications.

Article Details

How to Cite
[1]
M.Bhavsingh, K Venkatesh Sharma, and Mustafa Abdulkareem Salman Al-Nuaimi, “Integrating GAN-Based Image Enhancement with YOLOv5 Object Detection for Accurate Vehicle Number Plate Analysis”, Int. J. Comput. Eng. Res. Trends, vol. 10, no. 6, pp. 9–14, Jun. 2023.
Section
Research Articles

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