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Detection and Recognition of Vehicle License Plates using Deep Learning in Video Surveillance
Farooq, Muhammad Umer,Ahmed, Saad,Latif, Mustafa,Jawaid, Danish,Khan, Muhammad Zofeen,Khan, Yahya International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.11
The number of vehicles has increased exponentially over the past 20 years due to technological advancements. It is becoming almost impossible to manually control and manage the traffic in a city like Karachi. Without license plate recognition, traffic management is impossible. The Framework for License Plate Detection & Recognition to overcome these issues is proposed. License Plate Detection & Recognition is primarily performed in two steps. The first step is to accurately detect the license plate in the given image, and the second step is to successfully read and recognize each character of that license plate. Some of the most common algorithms used in the past are based on colour, texture, edge-detection and template matching. Nowadays, many researchers are proposing methods based on deep learning. This research proposes a framework for License Plate Detection & Recognition using a custom YOLOv5 Object Detector, image segmentation techniques, and Tesseract's optical character recognition OCR. The accuracy of this framework is 0.89.
Mohammed Nabil A.,Afzal Muhammad,Al-Faifi Sulieman A.,Khan Muhammad A.,Refay Yahya A.,AL-Samin Bazel H.,Alghamdi Salem S.,Ibrahim Abdullah 한국식물생명공학회 2023 Plant biotechnology reports Vol.17 No.4
Lentil is an important annual food legume crop, nitrogen fixer and provides a substantial amount of protein, carbohydrate, minerals, and vitamin content. The use of molecular markers to assess genetic diversity is crucial for crop improvement, efficient management, and conservation of plant genetic resources. The current study aimed to determine the genetic diversity among lentil genotypes using sequence-related amplified polymorphism (SRAP) markers. Therefore, we evaluated a collection of 36 lentil genotypes, including 20 from Yemen, Saudi Arabia (7), Egypt (4), and Bangladesh (3), and (2) genotypes from the International Center for Research in Dry Area (ICARDA) using 21 SRAP primer combinations. The amplified fragments showed a high level of useful polymorphic amplified fragments (775 out of 782) indicating a higher degree of variation. The polymorphic information content (PIC) ranged from 0.31 to 0.39 with an average of 0.33 for each primer. The UPGMA trees, based on Jaccard similarity index matrices, separated the genotypes into four main clusters according to their geographical origin. The population structure supported the major groups and attested to their great degree of differentiation. The highest lentil population was found at K = 3, K = 5, and K = 7 levels, showing purity and admixture ancestry among the lentil population. This study highlighted the wide genetic diversity among the studied lentil genotypes and demonstrated the effectiveness of the SRAP technique in determining the genetic variability of lentil. Furthermore, it could be used to establish the genetic peculiarity of ecotypes when applying for the obtainment of origin and agro-morphological characteristics.