License Plate Detection for Smart Parking Management

Automatic License Plate Detection and Recognition (ALPD-R) is an important and challenging application for traffic surveillance, traffic safety, security, services purposes and parking management. Generally, traditional image processing routines have been used in ALPD-R. Although the general approaches perform well on ALPD-R, new and efficient approaches are needed to improve the detection accuracies. Thus, in this paper, a new approach, which is based on fusing of multiple Faster Regions with Convolutional Neutral Network (Faster- RCNN) architectures, is proposed. More specially, the Deep Learning (DL) is used to detect license plates in given images. The proposed license plate detection method uses three Faster- RCNN modules where each faster RCNN module uses a pre-trained CNN model namely AlexNet, VGG16 and VGG19. Each Faster-RCNN module is trained independently and their results are fused in fusing layer. Fusing layer use average operator on the X and Y coordinates of the outputs of the Faster-RCNN modules and maximum operator is employed on the width and height outputs of the Faster-RCNN modules. A publicly available dataset is used in experiments. The accuracy is used as a performance indicator of the proposed method. For 100 testing images, the proposed method detects the exact location of license plates for 97 images. The accuracy of the proposed method is 97%.

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Parking entrance control using license plate detection and recognition

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v15.i1.pp476-483 ◽

2019 ◽

Vol 15 (1) ◽

pp. 476 ◽

Cited By ~ 1

Author(s):

Mohamed Sayed Farag ◽

Mostafa Mohamed Mohie El Din ◽

Hassan Ahmed Elshenbary

Keyword(s):

Recognition Rate ◽

Correlation Method ◽

Good Method ◽

Color Filter ◽

License Plate ◽

Image Size ◽

Smart Systems ◽

Smart Parking ◽

License Plate Detection ◽

Detection And Recognition

<p>There is no doubt that car parking is a very challenging and interesting topic of surveillance. In the recent years, a lot of smart systems for parking lot access control were developed to control and register the car data. The aim of this paper is to use image processing methods to control the entrance of a smart parking. The steps of car plate recognition are: preprocessing, License plate detection, character extraction and recognition. In the step of preprocessing, image was enhanced and noise was reduced. After preprocessing stage, color filter was used to detect the plate region. In case of large image size DWT was used for feature extraction and decreased the time of the detection stage. In the stage of character segmentation, the image is converted from grayscale to binary according to a given threshold. Filtering the binary image after using the morphological operation method, the largest objects are determined as the segmented plate characters. Finally, the correlation method was used to recognize the segmented characters. In case of similarity, SVM was used as a good classifier. Experimental results using matlab software, view that the proposed method increase the plate detection and recognition rates. It achieved aver- age 97.8% detection rate, 98% segmentation rate and 97% recognition rate, So it will be a good method for smart parking entrance control.</p>

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Bengali License Plate Detection using Viola-Jones Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b7689.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 4058-4065

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Road Safety ◽

License Plate ◽

Learning Techniques ◽

Wide Range ◽

Previous Decade ◽

Parking Management ◽

License Plate Detection ◽

Individual Classifier

Image classification has been a rapidly developing field over the previous decade, and the utilization of Convolutional Neural Networks (CNNs) and other deep learning techniques is developing rapidly. However, CNNs are compute-intensive. Another algorithm which was broadly utilized and keeps on being utilized is the Viola-Jones algorithm. Viola-Jones adopts an accumulation strategy. This means Viola-Jones utilizes a wide range of classifiers, each looking at a different part of the image. Every individual classifier is more fragile than the last classifier since it is taking in fewer data. At the point when the outcomes from every classifier are joined, be that as it may, they produce a solid classifier. In this paper, we would like to develop a model that will be able to detect the Bengali license plates of, using the Viola-Jones Algorithm with better precision. It can be utilized for various purposes like roadside help, road safety, parking management, etc

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A Low-Cost On-Street Parking Management System Based on Bluetooth Beacons

Sensors ◽

10.3390/s20164559 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4559 ◽

Cited By ~ 1

Author(s):

Chi-Fang Chien ◽

Hui-Tzu Chen ◽

Chi-Yi Lin

Keyword(s):

Management System ◽

Low Cost ◽

Distance Estimation ◽

Magnetic Sensors ◽

License Plate ◽

City Governments ◽

Human Labor ◽

Parking Management ◽

Information And Communication ◽

Smart Parking

In recent years, many city governments around the world have begun to use information and communication technology to increase the management efficiency of on-street parking. Among various experimental smart parking projects, deployment of wireless magnetic sensors and smart parking meters are quite common. However, using wireless magnetic sensors can only detect the occupancy of parking spaces without the knowledge of who are currently using these parking spaces; human labor is still needed to issue the parking bills. In contrast, smart parking meters based on image recognition can detect the occupancy of parking spaces along with the license plate numbers, but the cost of deploying smart parking meters is relatively high. In this research, we investigate the feasibility of building an on-street parking management system mainly based on low-cost Bluetooth beacons. Specifically, beacon transmitters are installed in the vehicles, and beacon receivers are deployed along the roadside parking spaces. By processing the received beacon signals using Kalman filter, our system can detect the occupancy of parking spaces as well as the identification of the vehicles. Although distance estimation using the received signal strength is not accurate, our experiments show that it suffices for correct detection of parking occupancy.

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