Vehicle Plate Research Articles

Distinguishing license plate numbers using discrete wavelet transform technology based deep learning

Cars that violate the red light, and to increase the huge number of cars in violation, it is necessary to discover a system for identifying car plate numbers with the intervention of a computer, computer vision and neural networks segment and detail the number plates by designing regular algorithms to identify the number of license plates in violation. In this work, interest is in identifying the Iraqi car plate in order to know the place where the vehicle papers and the letters on which the vehicle depends and to know the location of the car were completed. The technique that was carried out in this work is to build new wavelets from polynomials by mathematical methods and discover a new algorithm using the MATLAB program to identify each number in the vehicle plate with a specific color by training a convolutional neural network (CNN) after analyzing the image using the new wavelets to identify the contents of the plate and good results have been reached. The accuracy level was reached with good values of up to 95%.

YOLO V5 for Vehicle Plate Detection in DKI Jakarta

The odd-even rule on vehicle number plates in DKI Jakarta aims to reduce congestion that occurs in DKI Jakarta. The application of these regulations is constrained by the limitations of the manual supervision function by officers. This problem can be overcome by implementing intelligence in the form of detecting number plate objects with the YOLO v5 algorithm and the character extraction process with Optical Character Recognition technology using Tesseract OCR. Object detection technology will detect objects in the form of vehicle plates. The OCR method can extract the characters on the number plate, the extraction results can be processed into parameter categorization so that the program can distinguish between vehicles that violate the rules and do not violate the rules automatically and more effectively and minimize errors. Based on this research, the average percentage of objects detected in each video is 92.38%, and the average confidence value obtained in object detection is between 75.55%. The success rate of the character extraction process on number plates is 95.45%, and the average proportion according to the detected number plate category is 97.2%. The implementation of the YOLO Algorithm has succeeded in detecting license plates with odd and even categories on videos that can provide signs and save violations of vehicles that violate the odd and even rules.

Optical System to Recognize Car Plate Ownership

The process development of the image processing can solve the problem of detection and recognition of the license plate by taking pictures of the cars and then recognizing them. Most traffic applications rely on automatic vehicle plate detection in parking lots, border control, speed control, etc. In this study, a smart visual system was presented to identify car plates in the College of Science for Girls - University of Baghdad parking lot. The work included distinguishing the car plate and identifying cars, whether they belonged to the college or not. This process was based on the Cascade Classifier method based on the Viola-Jones algorithm, and a database for all car plate features was stored in a file using the proposed method. The recognized car was compared with the characteristics of the database using Oriented FAST and Rotated BRIEF then features were extracted using Histograms of Oriented Gradients. The license plate is recognized when matching features are employed using the matching feature’s function. The results of congruence and discrimination were excellent and very highly efficient. The luminous intensity dependence is considered, as the work is based on the red band of the car's image.

Vehicle Number Plate Detection

This study introduces an enhanced version of a previously published genetic algorithm based technique to allow fast and accurate detection of the vehicle plate number independently of the used application. This paper is dedicated on an improved technique of OCR based license plate recognition using neural network trained dataset of object features. A blended algorithm for recognition of license plate is proposed and is compared with existing methods for improve accuracy. The whole system can be categorized under three major modules, namely License Plate Localization, Plate Character Segmentation, and Plate Character Recognition. The system is simulated on 300 national and international motor vehicle LP images and results obtained justifies the main requirement

Automatic Number Plate Recognition Application for Metropolitan Toll Road Payment System

Many factors cause congestion on toll roads, one of which is service time at toll gates. Maximum service times for each vehicle be regulated by Indonesia Toll Authority to no more than 6 seconds for the open transaction toll system. There-fore, an electronic toll road payment system integrated with ANPR (Automatic Number Plate Recognition) technology is proposed. The proposed system can identify vehicle plates automatically to make payments. The Optical Character Recognition (OCR) method was chosen in this study for recognizing the number plate. ANPR with Tesseract OCR is placed inside the computer. Based on the results of the analysis and testing that has been carried out, the low average accuracy of 63,14% from this research is small due to the small number of sample images. The average execution times of 1405 milliseconds from this research are be-low the maximum 6 seconds limit for open transaction toll collection in Indonesia. One failed plate localization from sample images has founded in a car image with number plate DK 1547 EC.

Is Vehicle Plate Corner Prediction by Vision Transformer Better than CNNs?

The license plate recognition performance can be improved by converting the license plate photographed from the side to the front view. To perform this transformation, four vertex corner positions of the license plate are required. Existing deep learning methods to find these corner positions use a convolutional neural network (CNN). In this study, we propose a model using a vision transformer (ViT), a nonconvolutional method that has recently been attracting attention, as a backbone, and compare its performance with existing CNN models. The ablation study is conducted by diversifying the ViT structure. Through these results, it was found that ViT has strengths in model size reduction but is similar in performance or inferior to CNN and that ViT training is more difficult than CNN.

Studi Pendahuluan Sistem Keamanan Parkir Berbasis Opencv Di Kampus Universitas Indo Global Mandiri

Indo Global Indo Global Mandiri University, is one of the universities in Palembang City which has a fairly large parking area, so it is necessary to have a concept of comfort and security for the academic community and also guests in placing their vehicles in the parking area within the IGM University Campus. This study tries to provide an alternative how to build a vehicle parking concept, especially cars, by detecting vehicle plates and who owns it, so that it can be known early on vehicles that enter an unknown parking area, so that it can be known whether guests are or are treated as people who are not clear. the goal. The research model was built using the Python Programming Language with the OpenCV (Open Source Computer Vision) library as an image processor which with a process or several stages can change the car license plate in the form of an image into a string (word), then by connecting it to the vehicle owner's database. In order to detect who owns the car that will park. The results of the preliminary research, by detecting the vehicle plate in the form of a car image, the results obtained that the area code and plate number can be detected, but other images are also detected. The results show that the plate number is detected as BG 1359 UJ, converted to BG' 13:19 Q UJ. There were 2 conversion failures, namely the number 5 was changed to :1 and the small white circle (bolt) was changed to Q.Keywords: Parking, Detection, Vehicle Plate and OpenCV

Vehicle Plate Number Localization Using Memetic Algorithms and Convolutional Neural Networks

This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers (VPLN) in challenging image datasets. Since binarization of the input image is the most important and difficult step in the detection of VPLN, a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects (CCO) and hence enriches the solution space with more solution candidates. Due to the combination of the outputs of the three binarization techniques, many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate solutions. The pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate prototype. Using any of the previous versions will give moderate results but with very low speed. Hence, a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate symbols. The memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used techniques. Also, a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar patterns. Various image samples with a wide range of scale, orientation, and illumination conditions have been experimented with to verify the effect of the new improvements. Encouraging results with 97.55% detection precision have been reported using the recent challenging public Chinese City Parking Dataset (CCPD) outperforming the author of the dataset by 3.05% and the state-of-the-art technique by 1.45%.

Key Factors, Planning Strategy and Policy for Low-Carbon Transport Development in Developing Cities of China.

Exploring key impact factors and their effects on urban residents' transport carbon dioxide (CO2) emissions is significant for effective low-carbon transport planning. Researchers face the model uncertainty problem to seek a rational and better explanatory model and the key variables in the model set containing various factors after they are arranged and combined. This paper uses the Bayesian Model Averaging method to solve the above problem, explore the key variables, and determine their relative significance and averaging effects. Beijing, Xi'an, and Wuhan are selected as three case cities for their representation of developing Chinese cities. We found that the initial key factor increasing transport emissions is the high dependence on cars, and the second is the geographical location factor that much more suburban residents suffer longer commuting. Developing satellite city rank first for reducing transport emissions due to more local trips with an average short distance, the second is the metro accessibility, and the third is polycentric form. Key planning strategies and policies are proposed: (i) combining policies of car restriction based on vehicle plate number, encouraging clean fuel cars, a carbon tax on oil uses, and rewarding public transit passengers; (ii) fostering subcenters' strong industries to develop self-contained polycentric structures and satellite cities, and forming employment and life circle within 5 km radius; and (iii) integrating bus and rail transit services in the peripheral areas and suburbs and increasing the integration level of muti-modes transferring in transport hubs. The findings will offer empirical evidence and reference value in developing cities globally.

Enhancement of license plate recognition performance using Xception with Mish activation function.

The current breakthroughs in the highway research sector have resulted in a greater awareness and focus on the construction of an effective Intelligent Transportation System (ITS). One of the most actively researched areas is Vehicle Licence Plate Recognition (VLPR), concerned with determining the characters contained in a vehicle’s Licence Plate (LP). Many existing methods have been used to deal with different environmental complexity factors but are limited to motion deblurring. The aim of our research is to provide an effective and robust solution for recognizing characters present in license plates in complex environmental conditions. Our proposed approach is capable of handling not only the motion-blurred LPs but also recognizing the characters present in different types of low resolution and blurred license plates, illegible vehicle plates, license plates present in different weather and light conditions, and various traffic circumstances, as well as high-speed vehicles. Our research provides a series of different approaches to execute different steps in the character recognition process. The proposed approach presents the concept of Generative Adversarial Networks (GAN) with Discrete Cosine Transform (DCT) Discriminator (DCTGAN), a joint image super resolution and deblurring approach that uses a discrete cosine transform with low computational complexity to remove various types of blur and complexities from licence plates. License Plates (LPs) are detected using the Improved Bernsen Algorithm (IBA) with Connected Component Analysis(CCA). Finally, with the aid of the proposed Xception model with transfer learning, the characters in LPs are recognised. Here we have not used any segmentation technique to split the characters. Four benchmark datasets such as Stanford Cars, FZU Cars, HumAIn 2019 Challenge datasets, and Application-Oriented License Plate (AOLP) dataset, as well as our own collected dataset, were used for the validation of our proposed algorithm. This dataset includes the images of vehicles captured in different lighting and weather conditions such as sunny, rainy, cloudy, blurred, low illumination, foggy, and night. The suggested strategy does better than the current best practices in both numbers and quality.

Revealing transport inequality from an activity space perspective: A study based on human mobility data

Closing mobility and accessibility gaps between public transit riders and private car users is key to tackling social exclusion and achieving sustainable development goals (SDGs). However, place-based potential accessibility methods do not accurately measure real gaps in the uptake of activity opportunities because people usually have limited activity spaces. This study introduces people-based activity space approaches to measure activity disparities between the two modal groups. To overcome difficulties in obtaining large-scale individual activity data, this study used vehicle plate recognition data and public transit smart card data to anonymously identify activities. Individual activity spaces were characterised by six primary activity features from different dimensions. The analysis confirmed that, relative to transit riders, people who use cars on average accessed more activities within a larger activity space and enjoyed overall higher travel efficiency. A comprehensive indicator was further derived from the primary activity features to quantify activity disparities at the zone level. Zones with the highest risk of social exclusion were observed in the outskirts. In contrast, the city centre and inner suburbs exhibited significant equality of the two transport modes in fulfilling mobility needs for engagement in activities. Activity disparities between the two modalities were determined per area in specific activity dimensions, namely activity extensity, activity diversity, and travel efficiency. Finally, statistical models provided evidence that public transport facilities (especially rail transit) and location factors (distance to the city centre) are essential in determining modality-associated gaps in access to urban activity opportunities. Socioeconomic status and land use diversity also partially contributed to the inequality in specific dimensions of the activity space. This people-centred approach is critical for tackling transport inequality and achieving SDGs while “leaving no one behind”.

Plate Number Recognition based on Hybrid Techniques

Globally and locally, the number of vehicles is on the rise. It is becoming more and more challenging for authorities to track down specific vehicles. Automatic License Plate Recognition becomes an addition to transportation systems automation. Where the extraction of the vehicle license plate is done without human intervention. Identifying the precise place of a vehicle through its license plate number from moving images of the vehicle image is among the crucial activities for vehicle plate discovery systems. Artificial intelligence systems are connecting the gap between the physical world and digital world of automatic license plate detection. The proposed research uses machine learning to recognizing Arabic license plate numbers. An image of the vehicle number plate is captured and the detection is done by image processing, character segmentation which locates Arabic numeric characters on a number plate. The system recognizes the license plate number area and extracts the plate area from the vehicle image. The background color of the number plate identifies the vehicle types: (1) White color for private vehicle; (2) red color for bus and taxi; (3) blue color for governmental vehicle; (4) yellow color for trucks, tractors, and cranes; (5) black color for temporary license; and (6) green color for army. The recognition of Arabic numbers from license plates is achieved by two methods as (1) Google Tesseract OCR based recognition and (2) Machine Learning-based training and testing Arabic number character as K-nearest neighbors (kNN). The system has been tested on 90 images downloaded from the internet and captured from CCTV. Empirical outcomes show that the proposed system finds plate numbers as well as recognizes background color and Arabic number characters successfully. The overall success rates of plate localization and background color detection have been done. The overall success rate of plate localization and background color detection is 97.78%, and Arabic number detection in OCR is 45.56 % as well as in KNN is 92.22%.

Unsupervised Domain Adaptive Corner Detection in Vehicle Plate Images.

Rectification of vehicle plate images helps to improve the accuracy of license-plate recognition (LPR). It is a perspective-transformation process to project images as if taken from the front geometrically. To obtain the projection matrix, we require the (x, y) coordinates of four corner positions of plates in images. In this paper, we consider the problem of unsupervised domain adaptation for corner detection in plate images. We trained a model with plate images of one country, the source domain, and applied a domain adaptation scheme so that the model is able to work well on the plates of a different country, the target domain. For this study, we created a dataset of 22,096 Korea plate images with corner labels, which are source domain, and 6762 Philippines, which are target domain. To address this problem, we propose a heatmap-based corner-detection model, which outperforms existing scalar-regression methods, and an image classifier for mixed image of source and target images for domain adaptation. The proposed approach achieves better accuracy, which is 19.1% improvement if compared with baseline discriminator-based domain adaptation scheme.

Iraqi License Plate Recognition Based on Neural Network Technique

License Plate (LP) is the unique identification of a vehicle. License Plate Recognition (LPR) is considered as one of the promising aspects of applying computer seeing technology across intelligent transportation system. We used mathematical morphology and edge detection as a method for segmentation and extracting the vehicle license plate character, in Location of the vehicle plate. Initially, the color image was changed to a gray image, and by calculating the difference between the pixels and their neighbors in order to build the edge of the image. This makes the license panel appear clearly. The Sobel operator is applied to extract the edge of objects in the image. In order to obtain a smooth image, we apply the mathematical morphology of the images for dilation and erosion, and then we improve the efficiency of segmentation processing by using the images that are sent to the LP recognition stage. Multi-layer perceptron neural network will be used to recognize the license plate. Characters can be accurately extracted from the license plate due to the accurate extraction of the plate region.

Vehicle Number Plate Detection System for Indian Vehicle using OCR

Automatic Plate recognition is one of the techniques used for vehicle identification purposes and it will provide all details about particular vehicle owner This process usually comprises of three steps. First step is the vehicle plate localization, regardless of the vehicle-plate size and orientation. The second step is the segmentation of the characters and last step is the recognition of the characters from the vehicle plate. Thus, this project uncovers the fundamental idea of various algorithms required to accomplish character recognition from the vehicle plate. This feature of the algorithm mentioned above helped in achieving faster character recognition of the license plate. This process of character recognition consists of steps like Image processing, Defragmentation, Resizing and Character localization that are required to be performed on the image.

Anchoring effects in repeated auctions of homogeneous objects: Evidence from Macao

We provide evidence of anchoring effects from the sealed-bid auctions of vehicle plates in Macao. Vehicle plates are homogeneous in use value and differ only in character and numerical sequences. Nevertheless, vehicle plates with particular numerical sequences are consistently and repeatedly auctioned off in Macao, thus providing unique panel data that facilitate the identification of an anchoring effect. We adopt a hedonic pricing model to estimate the rational price of vehicle plates. We find that when bidding on a numerical sequence, bidders anchor on the winning bid in the previous auction, which result in continuation of the winning bid price.

Parking System Optimization Based on IoT using Face and Vehicle Plat Recognition via Amazon Web Service and ESP-32 CAM

Today's technology has developed rapidly. One application of technology is in the parking lot. Most parking lots in Indonesia can already recognize the vehicle plate image, but it is hoped that it can be even better by applying Internet of Things (IoT) technology that is integrated with facial recognition images. One of the parking problems is in the parking lot at the Sumatran Institute of Technology, where checking is still done manually by security officers. This of course will take time and the level of security is also not good, because when you enter there is no checking. Checks are only carried out at the time of exit and the officer who checks is not necessarily the same and memorized as the owner of the vehicle. The addition of this facial image recognition feature is expected to increase the security of the parking system. Facial image recognition can be assisted by Cloud services from Amazon Image Recognition. With this service, no training data is required. The system developed is only a prototype. The developed parking system can recognize facial images and vehicle license plates with 2 cameras using the ESP32-Cam when entering and exiting the parking lot. The use of the ESP32-cam can recognize facial images both during the day and at night. The results obtained by the system can work effectively with an increase of 21 %.

Automatic VLP’S Recognition For Smart Parking System

The system is designed as the authorized automatic vehicle identifier with respect to vehicle owner identification, for the security of the highly restricted areas like housing areas, defence military areas, parliament areas etc. Both of the number plate recognition and the vehicle owner recognition use the image identification technology. The image of the vehicle plate number and the vehicle owner are captured by using a camera and processed with MATLAB. The system contains an embedded section which controls the opening of the security gate and the communication purpose. The user interface provided at the entry/exit gate gives visual assistance to the driver, whether he/she is not permitted at this area or permitted. The entry of the unauthorized vehicle is restricted and the message is sent to the actual vehicle owner which is preregistered in the systems‟ database. The technology adopted in this research can be utilized not only for the security but also in other fields such as parking fee collection, automatic speed control, tracking stolen cars, automatic toll management and access control to limited areas, etc.

Barriers to the Implementation of Electronic Tickets

Abstract:This study aims to determineforms of obstacles in the implementation of electronic ticketing. The method used in this research is empirical or sociological research methodology. This study uses a qualitative approach and sampling using purposive sampling. The results of this study showthat the obstacles in the implementation of the electronic ticket are several things that can affect the ticket not going as expected, including the lack of socialization where this will provide understanding to the public, especially for those road users to be obedient to traffic rules and be aware of function of the electronic ticket applied by law enforcement road traffic department. Furthermore, the second obstacle is that the vehicle plate does not match the owner, meaning that for example a vehicle has changed hands or for example the first party has sold its vehicle to a second party, so that once the second party violates traffic, the electronic ticket will only be sent to the first owner this is because the vehicle that has been purchased has not been reversed by the second party, so this can hinder the passage of the electronic ticket. While the third obstacle in electronic ticketing is the existence of motorbike rentals, where when the motorbike borrower commits a violation, the ticket will only be sent to the motorbike owner from the first party. Therefore, this will make it difficult for law enforcement to impose sanctions on traffic violators through electronic tickets. where when a motorbike borrower commits a violation, a ticket will only be sent to the motorbike owner from the first party. Therefore, this will make it difficult for law enforcement to impose sanctions on traffic violators through electronic tickets. where when a motorbike borrower commits a violation, a ticket will only be sent to the motorbike owner from the first party. Therefore, this will make it difficult for law enforcement to impose sanctions on traffic violators through electronic tickets.

Android application for identification of vehicle plates for traffic inspection

Android application for identification of vehicle plates for traffic inspection