License Plate Detection and Facial Analysis Using TensorFlow Deep Learning Algorithm

2019 ◽  
Vol 16 (8) ◽  
pp. 3246-3251
Author(s):  
P. Manoj Prakash ◽  
Sreerag Premanathan ◽  
ShivamKumar Surwase ◽  
M. S. Antony Vigil ◽  
Shivam Bohare
Keyword(s):  
Learning Algorithm ◽  
Ccd Camera ◽  
License Plate ◽  
License Plate Recognition ◽  
Facial Analysis ◽  
Deep Learning Algorithm ◽  
Canny Edge ◽  
License Plate Detection ◽  
Toll Collection ◽  
Improved Technology

Nowadays license plate recognition has been applied in car access control, toll collection and other applications. Even though they exist, car thefts and illegal use of other proprietor’s license plate remain a problem. To deal with this, a computational programmed controlled framework is being developed. Also, facial analysis algorithm is implemented so as to create awareness among the common public. The way forward is to use an improved technology combination of License Plate Detection and Facial Analysis using artificial intelligence, in which vehicle image is captured by high resolution CCD camera and the license plate region is determined by image processing algorithms and facial analysis is done by using FaceNet and TensorFlow. The characters of the license plate is separated by segmentation and processed using the Canny Edge and Blob Coloring algorithm and the facial analysis is done using Facenet of TensorFlow.

scholarly journals Iraqi License Plate Detection and Segmentation based on Deep Learning

2021 ◽  
Vol 17 (2) ◽  
pp. 102-107
Author(s):  
Ghida Abbass ◽  
Ali Marhoon
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
K Nearest Neighbors ◽  
Deep Learning Algorithm ◽  
Learning Techniques ◽  
License Plate Detection ◽  
Accuracy Of Results ◽  
Speed And Accuracy

Nowadays, the trend has become to utilize Artificial Intelligence techniques to replace the human's mind in problem solving. Vehicle License Plate Recognition (VLPR) is one of these problems in which the computer outperforms the human being in terms of processing speed and accuracy of results. The emergence of deep learning techniques enhances and simplifies this task. This work emphasis on detecting the Iraqi License Plates based on SSD Deep Learning Algorithm. Then Segmenting the plate using horizontal and vertical shredding. Finally, the K-Nearest Neighbors (KNN) algorithm utilized to specify the type of car. The proposed system evaluated by using a group of 500 different Iraqi Vehicles. The successful results show that 98% regarding the plate detection, and 96% for segmenting operation.

Dynamic License Plate Localization for Vehicles on Multi-lane Using Single Camera

2013 ◽  
Vol 300-301 ◽  
pp. 740-745
Author(s):  
Hung Li Tseng ◽  
Chao Nan Hung ◽  
Chiu Ching Tuan ◽  
You Ru Wen ◽  
Wen Tzeng Huang ◽  
...  
Keyword(s):  
High Resolution ◽  
Recognition System ◽  
License Plate ◽  
Single Camera ◽  
License Plate Recognition ◽  
Hardware Cost ◽  
Localization System ◽  
Parking Management ◽  
Toll Collection ◽  
Localization Systems

LPR (License Plate Recognition) System has been widely used in highway toll collection, parking management, various traffic regulations enforcement and other systems. Currently, most of the existing LPL (license plate localization) systems are with single camera that is limited to recognizing vehicles in one lane. In this paper we design a license plate localization system that simultaneously recognizes license plates of vehicles on multi-lane by using single high-resolution camera. Our approach significantly reduces the hardware cost of LPR system without sacrificing the accuracy of recognition. And our success rate is about 94%.

scholarly journals IRAQI LICENSE PLATE RECOGNITION BASED ON MACHINE LEARNING

2020 ◽  
Vol 3 (4) ◽  
pp. 1-10
Author(s):  
Dunya A. Abd Alhamza ◽  
Ammar D. Alaythawy
Keyword(s):  
Machine Learning ◽  
Traffic Control ◽  
Character Recognition ◽  
Training Data ◽  
Correct Prediction ◽  
License Plate ◽  
License Plate Recognition ◽  
K Nearest Neighbors ◽  
License Plate Detection ◽  
Detection Search

 The license plate recognition (LPR) is an important system. LPR is helpful in many ranges such as private or public entrance, parking lots, traffic control and theft surveillance. This paper, offers (LPR) consist of four main stages (preprocessing, license plate detection, segmentation, character recognition) the first stage takes a photo by the camera then preprocessing in this image. License plate detection search for matching of license plate in the image to crop the correct plate. Segmentation performed by divide the numbers separately. The last stage is number recognition by using KNN (K- nearest neighbors) is one of the simple algorithms of machine learning used for matching numbers with training data to provide a correct prediction. The system was implemented using python3.5, open-cv library and shows accuracy performance result equal to 90% by using 50 images.

scholarly journals A design of license plate recognition system using convolutional neural network

2019 ◽  
Vol 9 (3) ◽  
pp. 2196
Author(s):  
P. Marzuki ◽  
A. R. Syafeeza ◽  
Y. C. Wong ◽  
N. A. Hamid ◽  
A. Nur Alisa ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Recognition System ◽  
Network Size ◽  
License Plate ◽  
License Plate Recognition ◽  
Sobel Operator ◽  
Proposed Model ◽  
Best Parameters

This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy.

scholarly journals Vehicle License Plate Detection for Parking Offenders Using Automatic License-Plate Recognition

2020 ◽  
Vol 55 (4) ◽  
Author(s):  
Chuan Pratama ◽  
Suci Aulia ◽  
Dadan Nur Ramadan ◽  
Sugondo Hadiyoso
Keyword(s):  
Detection Methods ◽  
Prototype System ◽  
License Plate ◽  
License Plate Recognition ◽  
Test Results ◽  
Accuracy Rate ◽  
Road Space ◽  
Camera Position ◽  
License Plate Detection ◽  
Automatic License Plate Recognition

Vehicles parked illegally on the highway can limit road space and result in congestion. Thus, illegal parking must be monitored and controlled. In this study, a prototype system for detecting the license plates of parking offenders based on image processing was implemented. The first stage in this system is detecting the license plate, then segmenting each character into a separate image. The next stage is converting the character from image to text format, referred to as automatic license-plate recognition. The goal is to send that detected plate license to the database of the authorities, so that the authorities can discover the identity of the parking offender to impose sanctions. In this study, several conditions of acquisition and variations of edge detection methods were tested. Based on the test results, an accuracy rate of 100% was obtained for license plate detection using the Canny method during the morning, with the camera position at 3 meters high, 2 meters of distance, and a 60o angle.

IMAGE PROCESSING OF ANDROID-BASED PATROL ROBOT FEATURING AUTOMATIC LICENSE PLATE RECOGNITION

2015 ◽  
Vol 3 (3) ◽  
pp. 296-301
Author(s):  
S. Madhan ◽  
M. Pradeep
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
License Plate ◽  
License Plate Recognition ◽  
Vertical Edge ◽  
Stain Removal ◽  
License Plate Detection ◽  
Novel Methods ◽  
Automatic License Plate Recognition

This work develops an Android-based robot featuring automatic license plate recognition and automatic license plate patrolling. The automatic license plate recognition feature combines 4 self-developed novel methods, Wiener deconvolution vertical edge enhancement, AdaBoost plus vertical-edge license plate detection, vertical edge projection histogram segmentation stain removal, and customized optical character recognition. Besides, the automatic license plate patrolling feature also integrates 3 novel methods, HL2-band rough license plate detection, orientated license plate approaching, and Ad-Hoc-based remote motion control. Implementation results show the license plate detection rate and recognition rate of the Android-based robot are over 99% and over 98%, respectively, under various scene conditions. Especially, the execution time of license plate recognition, including license plate detection, is only about 0.7 second per frame on the Android-based robot.

scholarly journals Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection

2020 ◽  
Vol 17 (3) ◽  
pp. 0909
Author(s):  
Bydaa Ali Hussain ◽  
Mohammed Sadoon Hathal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Edge Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Artificial Neural

            In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to identify and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully detects LP and recognizes multi-style Arabic characters with rates of 96% and 97.872% respectively under different conditions.

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