Vehicle License Research Articles

Perbandingan Kinerja Algoritma YOLO Dan RCNN Pada Deteksi Plat Nomor Kendaraan

Vehicle license plate detection is a system used to automatically recognize and identify license plates on passing vehicles. This system is useful to assist in managing traffic, reducing crime, and increasing the efficiency of the transportation system. However, in its implementation, license plate detection often encounters difficulties due to variations in shape, color, and lighting conditions. In addition, vehicle license plates are also cut off or distorted in digital images, which can complicate the detection process. To solve these problems, an effective method is needed to identify objects from digital images. This study aims to evaluate and compare the performance of two popular algorithms used for vehicle license plate detection, namely YOLO (You only look once) and RCNN (Region Convolutional Neural network). The performance evaluation of the two algorithms uses the same dataset so that their accuracy can be compared. The results showed that the YOLOv4 algorithm has a higher level of detection accuracy than RCNN with an accuracy of 96% and 87,8%, respectively. Based on these results it can be concluded that YOLOv4 is more suitable for use in vehicle license plate detection applications with prioritized speeds.

Character level vehicle license detection using multi layered feed forward back propagation neural network

Real-world traffic situations, including smart traffic monitoring, automated parking systems, and car services are increasingly using vehicle license detection systems (VLDS). Vehicle license plate identification is still a challenge with current approaches, particularly in more complicated settings. The use of machine learning and deep learning algorithms, which display improved classification accuracy and resilience, has been a significant recent breakthrough. Deep learning-based license plate identification using neural networks is proposed in this article. The number plate is detected using a multi layered feed forward back propagation neural network (MLFFBPNN). In this method, there are 3 layers namely input, hidden, and output layers has been utilized. Each layer has been related with interconnection weights. In feed forward of information, initially a set of randomly chosen weights are feed to the input data and an output has been determined. Back propagation training algorithm is utilized to train the network. Then character level identification is performed. The suggested proposed method is compared to the region-based convolutional neural network (RCNN) method in terms of accuracy and computational efficiency. The proposed method produced the character level recognition accuracy of 89%. It is improved by 4% when compared with the RCNN recognition method.

A Method for the Fusion of Results from RFID and VLPR

Radio Frequency Identification (RFID) and Vehicle License Plate Recognition (VLPR) are two main methods for vehicle identification. How to fusion the recognition results of the two methods is a problem that needs to be solved in practice. The existing fusion technology usually uses trigger acquisition for precise matching, or uses one recognition result as a reference to perform matching in another result set, while these technology does not provide a clearly algorithm or has certain limitations, and is not universal. A matching algorithm is presented in this paper, which takes advantage of the similarity metric between the results from RFID and VLPR, thus eliminating the drawbacks of existing match methods. The algorithm calculates the similarity metric of the results from RFID and VLPR by the vehicle license plate character graphics similarity, then find matching results from the two recognition results in a specific time window, finally work out precise matching result or similar matching result. The presented algorithm is simple but effective, with no need to triggering RFID and VLPR simultaneously. The algorithm can output both precise matches and similar matches. Through the verification results in the real road scene, the performance in the license plate similarity calculation and fusion matching process is consistent with expectations, which shows the effectiveness of the algorithm.

An efficient automated vehicle license plate recognition system under image processing

<span lang="EN-US">An automated vehicle license plate recognition system using image processing techniques identifies vehicle numbers without human interference. This system has significant impact because of its good application in various fields like car parking, access control, speed control, automatic toll collection, border security, traffic violence detection and surveillance applications. This paper presents a methodology that is quite simple but at the same time very much efficient and this system consists of four sequential modules which are preprocessing, number plate extraction, number plate character segmentation and character recognition. Preprocessing aims to improve the image quality that is captured in various illumination conditions and stick out outstanding information that we need, which is favorable to subsequent processing including extraction, segmentation and recognition. After preprocessing various morphological operations are applied to extract the desired license plate region. Then for segmentation the bounding box method is applied that segments each letter and number present on the license plate region. Finally, template matching is applied in identifying all segmented characters present in the license plate image. The experimental results showed that the proposed system can recognize license plate characters efficiently with higher accuracy. Using MATLAB software, the proposed method attains recognition accuracy of 94.17%.</span>

Driving factors and emission reduction scenarios analysis of CO2 emissions in Guangdong-Hong Kong-Macao Greater Bay Area and surrounding cities based on LMDI and system dynamics

As one of the most open and economically dynamic regions in China, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is at the forefront of low-carbon development and has an exemplary and leading role for other regions. This study provides a research framework based on the Logarithmic Mean Divisia Index (LMDI) and system dynamics (SD) by first compiling an inventory of CO2 emissions in the GBA and surrounding cities from 2000 to 2019 and then systematically and comprehensively analyzing the driving factors, future trends and policy implications of CO2 emissions in the GBA and surrounding cities. The results show that (a) CO2 emissions in the GBA and surrounding cities grew from 253.39 Mt in 2000 to 627.86 Mt in 2019, with an average annual growth rate of 4.89 %. The per capita CO2 emissions showed a continuous decreasing trend, and the overall carbon intensity of each sector showed a decreasing trend. (b) GDP per capita growth has the greatest effect on CO2 emissions, followed by the number of transport vehicles and population. The negative effects are energy intensity, average output of transportation vehicles, and residential energy intensity, with energy intensity being the most critical. (c) In the baseline scenario, regional CO2 emissions in 2030 are 1.25 times higher than those in 2019 and continue to grow. (d) Technological innovation measures are the most effective among individual emission reduction policies, followed by optimization of industrial structure. Furthermore, energy structure adjustment, vehicle licensing restrictions, and residents' green living are less effective. (e) Under comprehensive emission reduction measures, the region can achieve carbon emissions peaking in 2026 and reduce the regional carbon intensity by 66.24 % in 2030 compared with 2005. This study provides effective data support for the GBA and surrounding cities to formulate low carbon policies, promote carbon emission reduction and achieve carbon emissions peaking early.

Deep Learning Based Vehicle Speed Estimation on Highways

Traffic management always is a matter requiring the attention of highway system managers in terms of vehicle monitoring and speed estimation. This paper proposes an efficient deep learning-based vehicle speed estimation on highway lanes in the Vietnam transport system. The input videos are recorded by fixed surveillance cameras. An optimized single shot multibox detector network, called SSD, is utilized for vehicle license plate detection (LPD). The deep SORT (simple online and real-time tracking) model is first applied to video vehicle tracking and performed in the detected license plate area. This tracking process investigates the traveling distance of a vehicle to estimate its speed. In this study, the dataset has been normalized to improve the efficiency of vehicle localization and tracking to improve the time elapsing in the estimation of the distance traveled by vehicles on highways. The results showed that the proposed system has achieved better accuracy in terms of the determined speeds with errors ranging between [-1.5, +1.1] km/h, equivalent to 98% of the error limit by the regulation in Viet Nam.

Implementation of License Plate Recognition Monitoring System using Neural Network on Solar Powered Microcontroller

One automatic system for monitoring the presence of vehicles in a parking zone is an indispensable mean of an area such as services building, institutions, and other organizations, which accomodated many vehicles. A tracking record is the most important matter when the vehicle leaves the parking area. Manually, one parking officer will be needed to do the job. However, when using such an automated parking system, then this officers' job can be replaced. The vehicle license plate recording system is designed to use electronic-vision devices as a fundamental device for detecting the presence of vehicle. Vehicle license plates are detected using a digital camera which captured by the camera module on the Raspberry-Pi mini-pc microcontroller device, in addition to the detection of ultrasonic sensors that capture the position of vehicle objects. The process of reading vehicle numbers in an intelligent system of artificial neural networks to extracts each character of the license plate so that each number and letter can be recognized. Meanwhile, the detection of the ultrasonic parking sensor is a complementary confirmation indicating the presence of a vehicle object being monitored. The combination of solar power as the power supply for this automatic system is an important set-up that makes the system's electricity able to run independently. This monitoring system is prepared to help increase vehicle security automatically.

Névadás a rendszámtáblákon

License Plate Namings This research focuses on a specific onomastic phenomenon: the expression of human uniqueness on a surface for identification or advertising, such as vehicle license plates. The paper reveals the psychological and social drives relating to the selection of license plates and accounts for the intensity of emotional and sociolinguistic factors involved. Data collection took place in a settlement with a mixed Hungarian–Romanian population in Romania based on interviews. Motivational characteristics of the letters on the license plates: (i) names derived from personal names: 1 family names; 2 first names, bynames; 3 family names and first names or bynames; and (ii) names not derived from personal names: 1 names for institutions; 2 brand names; 3 other types of names; 4 random, nonpreferred serial license plate names. The number of observed names (214) does not allow us to draw far-reaching conclusions. Still, it provides insight into the cognitive processes involved. It sheds light on what kind of national, social and knowledge background gave birth to the different forms and on what messages necessary for the name givers are conveyed to the world by the characters chosen. One can observe diverse, imaginative, and creative names on licence plates.

E-recognition of Motorbike License Plate using Python and SQLite

Objectives: (1) To solve the problem of inefficiency and inaccuracy of vehicle monitoring; (2) To propose and develop a software that will serve as the initial replacement to a man-to-man motorbike monitoring; (3) Evaluate the components of the system in terms of functionality, efficiency, and accuracy. Methods: Waterfall model was used in the system development model. The proposal is a system development and evaluation. Python 3.6 was used in program coding, the Graphical User Interface (GUI) used was Tkinter, the database was SQLite, and the image processing OpenCV was utilized. KNN algorithm was used as the machine learning technique. Using Slovin’s formula in finding the sample of respondents with 95% of confidence level and 5 % margin error, resulted 33 respondents. The survey questionnaire is ISO 9126 Quality Software as the bases for software evaluation. Frequency distribution, percentage, and weighted mean were used to interpret the data gathered from the software evaluation metrics. Findings: Using the Likert Scale System, the weighted mean of efficiency is 4.25 described as Highly Acceptable, and the weighted mean of accuracy is 4.73 interpreted as Highly Acceptable. The overall mean is 4.49 interpreted as Highly acceptable. The functionality of the software using the percentage and frequency distribution is 100% functional. The proposal is a solution for inefficient and inaccurate of motorbike monitoring. The overall interpretation can be considered as recommendation for further study and additional features for advance version of vehicle license plate recognition. Limitations or non-recognizable pieces found in the license plate can be bearable for future studies recommended. Novelty: Initial Step in replacing the man-to-man motorbike License Plate Monitoring. The Author ‘s version in developing a software that will solve the inefficient and inaccurately monitoring of the status of the congested number of vehicles passing in and out in a certain vicinity, specifically motorbikes. Keywords: e-recognition; Motorbike; Man-To-Man Monitoring; Replacement; License Plate

Comparison of Vehicle License Plate Detection Algorithms and LP Character Segmentation and Recognition using Image Processing

In the last couple of decades, the number of vehicles has increased drastically, consequently, it is becoming difficult to keep track of each vehicle for purpose of law enforcement and traffic management. License Plate Detection is used increasingly nowadays for the same. The system performing the task of License Plate detection is known as the LPR system which generally consists of three steps: Detection of the License plate, Segmentation of License plate characters, and Recognition of the characters of the License Plate (LP). But in real-world scenarios, the various lighting conditions, camera angle, and rotation degrades the accuracy of License Plate region detection, which in turn causes inaccurate segmentation and recognition of the license plate characters hence leading to low accuracy of the LPR systems. Therefore, it is vital to consider the most promising algorithm or technique for LP detection. In this paper, we will be analyzing and comparing five different methods for license plate detection: Morphological reconstruction, Sobel Operator, Top Hat Transform, Histogram processing, and Canny Edge detection. We will be experimentally applying these techniques on real-time captured vehicle images, using the Bounding Box algorithm for character segmentation, performing license plate character recognition using Template matching, and subsequentially evaluating and demonstrating the LPR system that promises the most accurate and efficient results.

SIC - INTELLIGENT SYSTEM TO CONTROL THE ENTRY AND EXIT OF VEHICLES USING COMPUTER VISION AT FAMETRO UNIVERSITY CENTER

The security of physical and logical information are vital to any institution, which has to ensure availability, confidentiality and integrity of all types of information, be it logical or mainly property. The investments in the physical access control adopted must be directly linked to the importance of the assets to be protected, as in the parking of vehicles. And through the computer vision that covers several areas, specifically character identification, in this project the use of this technology aims to identify vehicle license plates, which can be used to authorize the entrance of vehicles. The main objective of this project is to intelligently automate the control of vehicles and increase the security of the internal environment, both for the patrimony and for the users themselves, making sure that only students and collaborators will use them. The developed system will add to the institution, having control over daily tasks, bringing security and statistical control through complete reports.

Penyalahgunaan Pembayaran Pajak Kendaraan Bermotor Pemerintah Berdasarkan Peraturan Daerah Nomor 6 Tahun 2010

Introduction: Motor vehicle tax is one type of tax that is increasing, in line with the needs of the state. In Law number 28 of 2009, concerning regional taxes and levies, it is explained that motor vehicle tax is a tax on ownership and control of motorized vehicles. The Maluku Provincial Government in responding to the authority given to manage finances issued Regional Regulation Number 6 of 2010 concerning Motor Vehicle Taxes.Purposes of the Research: This study aims to analyze, as well as review and discuss the misuse of government Motor Vehicle Tax based on regional Regulation No. 6 of 2010. Methods of the Research: The research method that is used with the type of normative research, in this writing is used to approach the problem of legislation and conceptual approach. Furthermore, the legal materials used are primary, secondary and tertiary legal materials. Procedures and collection of legal materials through the study of literature and processing of legal materials analysis, analysis is carried out using a description method using qualitative methods.Results of the Research: The results showed that the government Motor Vehicle Tax that occurs in the scope of Maluku province is the occurrence of abuse. This can be seen clearly in the case under review where one DPRD Office official did not replace the lat of the government vehicle number he bought from the local government. With the mode that by still using black motor vehicle license plates in order to pay less tax than other motor vehicles that have black license plates. Furthermore, it cannot be directly said that the taxpayer's fault, but also from the office in this case the Maluku Provincial Revenue Service which regulates government Motor Vehicle Tax.

Research of the influence of automobile purchase restriction policy on the pricing of motor vehicles in judicial appraisal enterprises

PurposeThe purpose of this paper is to take second-hand vehicles at judicial auctions in China as the primary research direction and to explore the impact of purchasing restriction policy and city size on the relationship between the appraisal price and transaction price of second-hand motor vehicles in the context of auto purchase restriction in China from a microscopic angle. It attempts to broaden the pricing ideas of judicial appraisal enterprises in providing appraisal prices of second-hand motor vehicles and to put forward suggestions for the optimization of appraisal prices and appraisal standards of judicial appraisal enterprises.Design/methodology/approachWith the help of Python, this paper crawls 59,038 lines of valid data from three leading internet judicial auction platforms, namely “Ali Auction,” “China Beijing Equity Exchange” and “Gong Pai Wang,” as research samples. Besides, this paper forms a database containing judicial auction used car appraisal prices, transaction prices, motor vehicle purchase restrictions and whether the motor vehicle carries a license plate. By constructing a multiple regression model, the impact of automobile purchase restriction policy on the price of motor vehicles appraised by judicial appraisal enterprises is investigated.FindingsWith the help of the multivariate regression model, it found that under the same condition, the city where the auction took place implemented the automobile purchase restriction before the end of the auction. The court has specified that the buyer could directly obtain the license plate after the auction. The transaction price and the evaluation price ratio will be statistically larger, which proves that the license plate has an evident value in the transaction and is traded as subject matter by the residents, and consequently brings a higher premium to the price of automobile transaction in internet judicial auction. Meanwhile, the purchase restriction policy in the first-tier cities has resulted in a significant premium on automobile license plates, which is much higher than the automobile license plate premium level in non-first-tier cities under the same conditions.Social implicationsCar ownership continues to rise with rapid economic development worldwide. Control the growth of car ownership, some countries and regions mainly restrict the issuance of motor vehicle license plates, which indirectly leads to vehicle license plate indicators becoming a scarce resource. National laws permit judicial auction as a means for the people's courts to settle creditors' claims in enforcement procedures of civil cases. In the judicial auction process, the People's Court introduces third-party evaluation enterprises to appraise, assess and audit the subject and obtain the appraisal price, which guides the bidding behavior of used car buyers and indirectly affects the transaction price of used cars.Originality/valueAs the only subject capable of assessing the value of used cars at judicial auctions, judicial appraisal enterprises have received widespread attention for their appraisal results. This paper researches this field by screening the factors affecting the ratio of motor vehicle transaction price to the appraised price. It also analyzes how the ratio of motor vehicle transaction price to appraised price is affected by motor vehicle purchase restrictions and the situation with license plates. This paper examines the existence of premiums for motor vehicle transactions with license plates, evaluates the purchase restrictions in cities with motor vehicle purchase restrictions and verifies that the premiums for motor vehicles at judicial auctions are affected by purchase restriction policies as well as the influence of city class. These studies have important implications for judicial appraisal enterprises to establish reasonable appraisal mechanisms and optimize appraisal prices. They also provide new ideas and methods for appraisal enterprises to assess the value of used vehicles at judicial auctions.

UK exceptional case driving application outcomes in post-stroke homonymous hemianopia: results from a clinical study

Purpose We report results in relation to returning to driving in the UK under the exceptional cases rule for visual field loss. Methods The Hemianopia Adaptation Study is a prospective clinical study recruiting adult stroke survivors with new onset homonymous hemianopia. The mobility assessment course (MAC) was used to measure navigational scanning. Car drivers were offered a 1-year post-stroke assessment to consider referral for driving assessment. Results Of 144 participants, 51 were eligible for driving assessment, with 13 (25.4%) accepting appointment for UK Driving and Vehicle Licensing Agency (DVLA) referral. A statistically significant difference in gender and baseline Barthel (stroke severity) scores was found between those requesting referral and those declining (p = 0.046; p < 0.001). MAC outcomes were significantly different, with those referred having a lower percentage of target omissions (9.0%) and faster mean course completion time (46.0 s), than those not referred (28.3%/72.5 s) (p = 0.006/p < 0.001). Twelve of the 13 referred were offered a driving assessment by the DVLA. All 12 passed and returned to driving. Conclusions It is possible for individuals with post-stroke homonymous hemianopia to return to driving, where exceptional cases criteria are met. There is evidence to support use of the MAC as a clinical measurement of adaptation. IMPLICATIONS FOR REHABILITATION Individuals with post-stroke homonymous hemianopia should be fully informed regarding driving regulations that can differ between countries and sometimes different states within a country, as well as provided with the support and opportunity to consider a return to driving if appropriate. Professionals providing care within the stroke multi-disciplinary team should be aware that it is possible for patients with homonymous hemianopia to return to driving, when exceptional cases criteria are met. The mobility assessment course (MAC) should be considered as a clinical measurement of adaptation in homonymous hemianopia. A cut-off score of ≤25% omissions on MAC could be employed to determine those likely to adapt to hemianopia long-term and potentially return to driving.

Application of Remote Sensing Methodology for Vehicle Emission Inspection

Remote sensing detection of vehicle emissions is an effective supplement to the statutory periodic inspection of in-use vehicle emissions and it is a convenient technical method for real-time screening of high-emission vehicles. The principle of remote sensing detection is to inversely calculate the absolute concentrations of gaseous pollutants in vehicle exhaust according to the relative volume concentration ratio of each exhaust component to carbon dioxide (CO2) in the vehicle exhaust plume. Because the combustion mechanisms of gasoline engines and diesel engines are different, different inversion calculation methods of remote sensing data must be applied. The absolute concentrations of gasoline vehicle gaseous emissions measured by remote sensing can be calculated by the inversion calculation method based on the theoretical air–fuel ratio combustion mechanism. However, the absolute concentrations of diesel vehicle nitrogen oxide (NOx) measured by remote sensing must be calculated by the inversion calculation method based on the correction of the excess air coefficient. For the integrated remote sensing test system of gasoline and diesel vehicles, it is necessary to determine the vehicle category according to the vehicle type and license plate and adopt different inversion calculation methods to obtain the correct remote sensing results of vehicle emissions. The big data statistical analysis method for vehicle emission remote sensing results can quickly screen high-emission vehicles and dynamically determine the remote sensing emission screening threshold of high-emission vehicles as the composition of in-use vehicles changes and the overall emission of vehicles declines, so as to achieve dynamic and accurate screening of high-emission vehicles.

Factors Associated with Driving Behavior and Road Traffic Accidents among Public Vehicle Drivers in Kathmandu Valley: A Cross-sectional Study

Introduction: Vehicle drivers are most significant stakeholder in any road traffic accident (RTAs). This study aims to assess their knowledge related to traffic signs and attitude towards safe driving practices and to identify self-reported risky driving behaviors, their encounter of RTAs, and associated factors.&#x0D; Methods: We carried out a cross-sectional study in Kathmandu valley. 14 prime spots were randomly selected and time location sampling was done. A face-to-face interview was done with public vehicle drivers using structured questionnaire. We analyzed the association using logistic regression. Ethical clearance was obtained from Institute of Medicine. Informed written consent was taken from the participants.&#x0D; Results: Of 411 drivers, mean age was 31.8 (±8.2) years. All participants were male of which 71.0% lived in a joint family. More than half had less than 10 year experience and 20% of them were driving 12-18 hours per day. Half of them had good knowledge on traffic signs while nearly one tenth had non-supportive attitude towards safe driving practices. Prevalence of at least one risky driving behavior was 68%, however, only a few cases fined by traffic. Self-reported encounters of RTA were 21.7%; of these, 22.2% also had human injuries. Experiences of driving less than 10 years and living in a nuclear family were significantly associated with risky driving. Moreover, age of public vehicle drivers less than 25 years, living in nuclear family and continuing driving while fatigue were also significantly associated with RTAs.&#x0D; Conclusion: Only half of drivers had good knowledge on traffic signs. Risky driving behavior present in about seven among ten public vehicle drivers and two among ten encountering RTAs. We recommend interventions targeted to change behavior among drivers to reduce risky driving behaviors, to increase age bar for permitting driving license for public vehicles and not continuing driving while fatigue to reduce RTAs.

UAV with Vision to Recognise Vehicle Number Plates

The trend of using Unmanned Aerial Vehicles (UAVs) in industries is rapidly growing. Nowadays, they are used in many applications, from surveillance to disaster assistance. Almost all applications of UAVs require cameras either to perform specific vision tasks such as facial recognition and vehicle number plate identification or to avoid obstacles in the flight path of the UAV. The most emerging application of UAVs today is to provide security and surveillance, and they are mostly used for vehicle number identification. Typical Automatic Number Plate Recognition (ANPR) uses static high-resolution cameras mounted in specific places to identify the vehicle’s number plates. Identifying the characters on the number plate becomes a very crucial task when the number plate is at an arbitrary angle to the drone camera. The camera gimble angle, the height of the UAV from the vehicle, and the relative speed of the UAV w.r.t vehicle play a very key role in identifying the vehicle license plate correctly. This study explains how Automatic Number Plate Recognition (ANPR) is performed on real-time images using MATLAB with the help of a UAV to analyze the effect of the above-mentioned key factors. The process is completed in three steps: collecting visual data from the drone, processing that data, and obtaining the recognized number plate.

Obstacles in adopting Lean Thinking at the Driver and Vehicle Licensing Authority in Ghana

The Lean Thinking ideology has established tools and techniques needed to reduce non-value-added activities such as waiting time, overproduction, transportation, motion, inventory and defects within an organization’s value stream. Numerous organizations across the world have applied Lean Thinking and have attained vast benefits. This research examined the obstacles or barriers to Lean implementation at the Driver and Vehicle Licensing Authority (DVLA) in Ghana. The research used the concurrent mixed methods research design. A total of  clients of DVLA were sampled for the study using a simple random sampling approach. The data analysis for the study was grounded on the standardized Z-test and thematic analysis. The results from the study revealed that lack of top management support, consulting cost in Lean, stringent requirements and approval, lack of knowledge in Lean, and lack of government support for research and collaboration in Lean were the barriers in the Lean implementation process. It was therefore recommended that management and employees of DVLA should focus on identifying and eliminating the obstacles in Lean implementation which affect their operations.

COMPARISON OF METHODS FOR AUTOMATIC LICENSE NUMBER RECOGNITION

The paper is devoted to the problem of automatic detection and recognition of license plates, the solution of which has many potential applications, from security to traffic management. The purpose of this work was to compare the methods of finding and recognizing car number plates, based on the application of deep learning algorithms, which takes into account different regional standards of car number plates, video quality, different speeds of vehicles, the location of the camera in relation to the vehicle license plate, defects of the car number plate (pollution , deformation), as well as changes in external lighting conditions. The advantages and disadvantages of localization and segmentation of car number plates on cars using image binarization, Viola–Jones and Harr methods are given. It was determined that adaptive approaches are better due to the possibility of compensating the impact of obstacles on different areas of the image, for example, the distribution of shadows due to the heterogeneity of illumination. It was determined that many methods in real algorithms rely directly or indirectly on the presence of number limits. Even if the limits are not used when the number is determined, they have the possibility to be used for further analysis. The methods of templates, image histograms, and contour analysis were compared to identify familiar features in the image (segmentation). It is shown that an effective approach for recognition of car license plates can be based on the application of the methods of Viola-Jones, Harr, the analysis of brightness histograms and the SVM method. Formulated conclusions on the effectiveness of the implementation of each of the procedures were confirmed as a result of conducting experiments with the developed software in the python 3 language using the cv2 computer vision library. The described approach makes it possible to obtain a fairly high recognition accuracy at different angles of rotation of the license plate relative to the camera. Keywords: automatic recognition, license plates, localization, normalization, segmentation, character recognition.

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.