Automated parking management system for identifying vehicle number plate

Singh, Asha Camper; Vaidya, Prasanth

doi:10.11591/ijeecs.v13.i1.pp77-84

Cited by 11 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional primarily based strategies by using Local Binary Pattern (LBP [11]) and Histogram of Oriented Gradient (HOG) [13]. The final result shows that our retrained CNN mannequin does a better job of classifying dog breeds.…”

mentioning

confidence: 92%

A novel Dog Breed Identification using Convolutional Neural Network

2022

PSEN

View full text Add to dashboard Cite

ML has grown in popularity over the previous decade as a result of strong computers that can process large amounts of data in a reasonable length of time. A well-known problem in Machine Learning is the Dog breed classifier. The issue is determining a dog's breed. The input can be a dog or a human image, and the proposed algorithm should be able to forecast the dog's breed or, if the human is a dog, which breeds it belongs to. For human face detection, OpenCV is utilised, while for dog face detection, the VGG16 model is employed. With the ResNet101 architecture, a convolutional neural network is utilized for classification. On test data, the final model had an accuracy of 81 percent.

show abstract

mentioning

confidence: 92%

A novel Dog Breed Identification using Convolutional Neural Network

2022

PSEN

View full text Add to dashboard Cite

show abstract

“…Using the IoT, researchers in [4] proposed a smart parking system using field-programmable gate arrays (FPGA) depending on emergency status. Singh and Vaidya [5] proposed an automated vehicle parking system to identify the license plate number to manage vehicle billing and parking by using image processing. While authors used one of the fundamental machine learning methods, kernel nearest neighbors (KNN), to recognize the license plate [6].…”

Section: Introductionmentioning

confidence: 99%

Efficient automated car parking system based modified internet of spatial things in smart cities

Alsaedi

Jalal

2023

IJEECS

View full text Add to dashboard Cite

The technological advances of smart cities have been progressively increasing to improve the quality of life to humans, especially in urban mobility. Parking appears to be a major issue, with residents needing to find a suitable parking space among many parking areas, resulting in time and fuel waste as well as environmental pollution. We propose in this paper a new automated system model that integrates reinforcement learning (RL), Q-learning, and image processing algorithms based on modified Internet of Spatial Things (IoST) architecture to optimize automated parking in smart cities. For demonstrating the efficiency of the proposed model, iFogSim simulation is used to reduce network usage and latency. Moreover, it deploys heterogeneous devices in multi layers and different scenarios. The experimental results show that the suggested system for automated car parking in fog-based placement-IoST network is feasible and effective. it minimizes latency and the total network usage compared to the cloud-based placement of the implemented system.

show abstract

“…Smart parking systems based on emergency status was proposed using FPGA to perform a lot of tasks like automatic parking depends on the behavior of driving and warning the drivers [2]. An automated parking management system,(APMS) for recognizing vehicles plate numbers in [3] based on template matching . In [4] the proposed work focused on providing a solution to vehicle parking Management System.…”

Section: Introductionmentioning

confidence: 99%

Deep learning versus traditional methods for parking lots occupancy classification

Farag

Din

Elshenbary

2020

IJEECS

View full text Add to dashboard Cite

<span>Due to the increase in number of cars and slow city developments, there is a need for smart parking system. One of the main issues in smart parking systems is parking lot occupancy status classification, so this paper introduce two methods for parking lot classification. The first method uses the mean, after converting the colored image to grayscale, then to black/white. If the mean is greater than a given threshold it is classified as occupied, otherwise it is empty. This method gave 90% correct classification rate on cnrall database. It overcome the alexnet deep learning method trained and tested on the same database (the mean method has no training time). The second method, which depends on deep learning is a deep learning neural network consists of 11 layers, trained and tested on the same database. It gave 93% correct classification rate, when trained on cnrall and tested on the same database. As shown, this method overcome the alexnet deep learning and the mean methods on the same database. On the Pklot database the alexnet and our deep learning network have a close resutls, overcome <br /> the mean method (greater than 95%).</span>

show abstract

Automated parking management system for identifying vehicle number plate

Cited by 11 publications

References 20 publications

A novel Dog Breed Identification using Convolutional Neural Network

A novel Dog Breed Identification using Convolutional Neural Network

Efficient automated car parking system based modified internet of spatial things in smart cities

Deep learning versus traditional methods for parking lots occupancy classification

Contact Info

Product

Resources

About