PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Evaluating the Performance of Ensembled YOLOv8 Variants in Smart Parking Applications for Vehicle Detection and License Plate Recognition under Varying Lighting Conditions
Version 1
: Received: 17 April 2024 / Approved: 18 April 2024 / Online: 18 April 2024 (10:48:07 CEST)
How to cite:
Singh, R.; Goyal, S.; Agarwal, S.; Upadhyay, S. Evaluating the Performance of Ensembled YOLOv8 Variants in Smart Parking Applications for Vehicle Detection and License Plate Recognition under Varying Lighting Conditions. Preprints2024, 2024041216. https://doi.org/10.20944/preprints202404.1216.v1
Singh, R.; Goyal, S.; Agarwal, S.; Upadhyay, S. Evaluating the Performance of Ensembled YOLOv8 Variants in Smart Parking Applications for Vehicle Detection and License Plate Recognition under Varying Lighting Conditions. Preprints 2024, 2024041216. https://doi.org/10.20944/preprints202404.1216.v1
Singh, R.; Goyal, S.; Agarwal, S.; Upadhyay, S. Evaluating the Performance of Ensembled YOLOv8 Variants in Smart Parking Applications for Vehicle Detection and License Plate Recognition under Varying Lighting Conditions. Preprints2024, 2024041216. https://doi.org/10.20944/preprints202404.1216.v1
APA Style
Singh, R., Goyal, S., Agarwal, S., & Upadhyay, S. (2024). Evaluating the Performance of Ensembled YOLOv8 Variants in Smart Parking Applications for Vehicle Detection and License Plate Recognition under Varying Lighting Conditions. Preprints. https://doi.org/10.20944/preprints202404.1216.v1
Chicago/Turabian Style
Singh, R., Shivam Agarwal and Subho Upadhyay. 2024 "Evaluating the Performance of Ensembled YOLOv8 Variants in Smart Parking Applications for Vehicle Detection and License Plate Recognition under Varying Lighting Conditions" Preprints. https://doi.org/10.20944/preprints202404.1216.v1
Abstract
This paper investigates the efficacy of YOLOv8 variants for vehicle detection and license plate detection within smart parking applications, emphasizing performance under varying ambient lighting conditions. The proposed system is to seize full video frames, extracts regions of interest containing vehicles, and feeds them into separate, pre-trained YOLOv8 models – one dedicated to vehicle detection and another for license plate detection. Four YOLOv8 variants, nano, small, medium, and large, are evaluated. As a pre-processing step, the images are processed with the help of OpenCV and Pillow libraries to adjust the luminosity and increase the images’ DPI so that they would be easy to perceive by the Tesseract OCR engine. Sixteen potential combinations arise from pairing the four YOLOv8 models for vehicle and license plate detection tasks. To identify the most suitable combinations, we employ the Multi-Criteria Decision-Making method, specifically Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. This analysis considers 4 critical metrics: Precision, mean average precision at 95% Intersection of Union threshold, Recall and Total Inference Time. The objective is to achieve an optimal balance between high accuracy and real-time processing. Following the selection of optimal YOLOv8 combinations through TOPSIS analysis, we assess their performance under varying ambient light intensity (measured in lux). This evaluation aims to identify the most robust model combinations that ensure accurate vehicle and license plate recognition across the diverse lighting conditions encountered in real-world environment.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
