scholarly journals A COMPARISON PRE-TRAINED MODELS FOR AUTOMATIC INDONESIAN LICENSE PLATE RECOGNITION

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Sahid Bismantoko ◽  
M. Rosyidi ◽  
Umi Chasanah ◽  
Asep Haryono ◽  
Tri Widodo
Keyword(s):  
Image Processing ◽  
Law Enforcement ◽  
Intelligent Transportation System ◽  
Computation Time ◽  
Intelligent Transportation ◽  
Transportation System ◽  
License Plate ◽  
License Plate Recognition ◽  
Processing Approach ◽  
Optimum Model

Automatic License Plate Recognition is related to the Intelligent Transportation System (ITS) that supports the road's e-law enforcement system. In the case of the Indonesian license plate, with various colour rules for font and background, and sometimes vehicle owners modify their license plate font format, this is a challenge in the image processing approach. This research utilizes pre-trained of AlexNet, VGGNet, and ResNet to determine the optimum model of Indonesian character license plate recognition. Three pre-trained approaches in CNN-based detection for reducing time for a build if model from scratch. The experiment shows that using the pre-trained ResNet model gives a better result than another two approaches. The optimum results were obtained at epoch 50 with an accuracy of 99.9% and computation time of 26 minutes. This experiment results fulfil the goal of this research. Keywords : ALPR; ITS; CNN; AlexNet; VGGNet; ResNet

scholarly journals License Plate Recognition System Using Matlab GUI For Surveillance

2018 ◽  
Vol 7 (2.7) ◽  
pp. 1008
Author(s):  
M Venkata Srinu ◽  
Venkateswara Rao Morla ◽  
Kali Vara Prasad Baditha ◽  
Vara Kumari. S ◽  
Srinivas Maddimsetti
Keyword(s):  
Image Processing ◽  
Template Matching ◽  
Intelligent Transportation System ◽  
Computation Time ◽  
Mobile App ◽  
Candidate Region ◽  
License Plate ◽  
Maximum Magnitude ◽  
License Plate Recognition ◽  
Plate Number

License plate recognition is an essential task in applications like urban vehicle management, intelligent transportation system, traffic surveillance and parking management system. In this work, we acquire the images using the mobile app and recognize license plate details with the help of our proposed image processing model. The recognized license plate details have been displayed on a customized website. The proposed image processing model does the adaptive thresholding on the images with resolution of 1280× 960. The connected component analysis using bounding boxes will be performed on threshold image. The desired plate details are highlighted by creating a region of interest for maximum magnitude row of an image based on the pixel value, then the statistical and logical operations are used to extract the candidate region. After obtaining candidate region, recognition of license plate number has been done using template matching. The complete customer details, displayed on the customized website which is connected to the database with the help of plate number. The computation time of proposed method is less than the existed methods. 

Development of intelligent transportation system for Philippine license plate recognition

2016 ◽  
Author(s):  
John Paolo D. Dalida ◽  
A-Jay N. Galiza ◽  
Aleck Gene O. Godoy ◽  
Masaru Q. Nakaegawa ◽  
Jean Louise M. Vallester ◽  
...  
Keyword(s):  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
License Plate ◽  
License Plate Recognition

The Application of Image Processing Technology in the Intelligent Transportation System

2014 ◽  
Vol 543-547 ◽  
pp. 2678-2680 ◽  
Author(s):  
Xiu Hua Teng
Keyword(s):  
Image Processing ◽  
Intelligent Transportation System ◽  
Mean Shift ◽  
Recognition System ◽  
License Plate ◽  
License Plate Recognition ◽  
Vehicle Recognition ◽  
Research Fields ◽  
Plate Location ◽  
Model Recognition

Image processing-based vehicle recognition is one of the important research fields in ITS. The existing methods are all based on license plate recognition and car shape recognition. Their common problem is algorithm stability. And the license plates are easy to be changed. All information about vehicles should be used to recognize them reliably. A problem to be solved is to find a method to recognize vehicles besides license plate recognition and vehicle model recognition. Vehicle license plate location and character segmentation are critical steps in the license plate recognition system, and yet there are difficult problems to be solved. Kernel density estimation and Mean Shift theory

Vehicle License Plate Recognition with Intelligent Materials Based on Color Division

2012 ◽  
Vol 485 ◽  
pp. 592-595
Author(s):  
Sheng Bing Che ◽  
Jin Kai Luo
Keyword(s):  
Light Intensity ◽  
Intelligent Transportation System ◽  
Color Space ◽  
Intelligent Transportation ◽  
License Plate ◽  
License Plate Recognition ◽  
Intelligent Materials ◽  
Plate Area ◽  
Rgb Color Space ◽  
New Algorithms

With the development and progress of the economic and technology, transportation has become more and more important in human’s usual life. Intelligent transportation system can be used in many areas. Images in RGB color space are very sensitive to the environment light intensity, and what’s more, there may be some pollution appeared on the plate and so on, these made it difficult to locate the plate area. In this paper, we proposed some new algorithms. In the first stage, car image preprocess, a new algorithm named color division was proposed. Experiments show that, influences from the environment as well as some inevitably differences of colors in the plate into standard color, so the performance of whole system was improved. In the period of license location, a new algorithm based on license color pairs was proposed, and it has good performance after experiments.

scholarly journals Autonomous Vehicular Surveillance using License Plate Recognition over Cloud Computing Architecture

2019 ◽  
Vol 9 (1) ◽  
pp. 4149-4153
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
Law Enforcement ◽  
Extraction Process ◽  
Scale Model ◽  
Support Vector ◽  
License Plate ◽  
License Plate Recognition ◽  
Video Cameras ◽  
Self Driving Cars

License Plate Recognition (LPR) is the extraction and identification of licence plate numbers from license plates. The extraction process requires ample image pre-processing using normalization, gray scaling and edge removal techniques. These extracted plates can then be identified using image processing techniques such as neural networks and support vector machines. These license plates are captured using stationary video cameras, which extracts images from their feed as inputs into the image extraction algorithm. For the purposes of vehicular surveillance, these cameras are inefficient, as a lot of them will be required to monitor vehicles effectively. Hence there is a need for a larger scale model to carry out effective vehicular surveillance. For this purpose, the cameras embedded in self driving cars are utilised as replacements to stationary video cameras. These cameras have to advantage of being constantly mobile, hence being able to carry out a larger scale of surveillance. These cameras capture meaningful images of license plates from their video feed, and upload these images to the cloud using a Vehicular Cloud Computing (VCC) architecture. This centralized cloud carries out the image extraction and image processing tasks. The identified license plates can be used to monitor the cars they belong to. The cloud compares them to a database of license plates that are flagged by law enforcement. If the license plate is found to be flagged, then the respective law enforcement authorities are notified of the location of the car. If the plate belongs to a car with a history of misbehavior, the car capturing the plate is informed of thus, making it easier to safely navigate around the problematic driver.

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