Car Plate Research Articles

Preparation of high-performance electroplated zinc coatings using modified DESs method

This study suggests a new process for zinc recycling in the iron and steel industry, that is, using RHF zinc-containing refined dust as the raw material and ChCl-urea DES (Reline) as the medium. The solid waste from the bottom-turning furnace is leached out, and the resulting zinc is plated directly onto automobile plates. This paper focuses on the utilization of EDTA as an additive to improve the overall performance of the zinc coatings. The results demonstrated that the addition of EDTA realized the modulation of the morphology of the coatings, which made the surface milky white and uniformly bright. The coating consists of hexagonal lamellar grains, dominated by (002) crystalline surfaces that grow at small angles, and the surface flatness is dramatically improved. Most importantly, the hardness of the modified coatings was increased by 70.0% and the coefficient of friction was reduced by about 29.2% compared with the coatings obtained in the pure solution. The addition of EDTA led to the formation of a surface passivation film during the corrosion process, which significantly improved the corrosion resistance of the material surface and the corrosion rate was reduced by 0.355 mm.A−1.

Egyptian car plate recognition based on YOLOv8, Easy-OCR, and CNN

This research presents an innovative approach to Egyptian car plate recognition using YOLOv8 and optical character recognition (OCR) technologies. Leveraging the powerful object detection capabilities of YOLOv8, the system efficiently detects car plates within images, videos, or real-time. Subsequently, OCR algorithms are applied to extract alphanumeric characters from the identified plates, facilitating accurate license plate recognition. The integration of YOLOv8 and OCR enhances the system's robustness in varying conditions, contributing to improved performance in real-world scenarios. This study advances the field of automatic license plate recognition, showcasing the potential for practical applications in traffic management, law enforcement, and security systems. A public dataset of Egyptian car plates is used for training and testing the model. Two OCR approaches are used and tested which proved their performance, while CNN-based approach reaches 99.4% accuracy.

A Robust License Plate Detection System Using Smart Device

The license plate recognition (LPR) system is widely employed in various applications. However, most research studies have used a fixed camera rather than a moving one. This is because the location of the vehicle plate is nearly static and easily estimated, making the use of a static camera simple for locating and detecting the scanned license plate. Images obtained with a moving camera are highly complex due to frequent background changes. Additionally, a challenge with car plates in Malaysia is their non-standardized nature. Car owners are permitted to use any font type for their license plate number, rendering existing license plate recognition systems from other countries incapable of effectively detecting license plates on Malaysian car plates. A traditional LPR system typically requires a high-quality camera and a powerful computer for costly and bulky processing. Nowadays, many smartphones come equipped with powerful processors and cameras. Android smartphones include various libraries for modifying hardware configurations such as the camera. This paper presents a robust method for detecting Malaysia's license plate number using a convolutional neural network (CNN). The CNN model from the pre-training process is imported to the Android device and tested in real-time in an on-road driving environment, resulting in an average recognition rate of 89.37%. A comprehensive Character Recognition Analysis is also presented to demonstrate the accuracy of each character. However, there is still room for improvement in recognizing the character Q.

Innovative deep eutectic solvent approaches in zinc electroplating for morphology enhancement, superior strength, and corrosion protection

The growing number of galvanized scrap due to the extensive application of galvanized products has led to a significant increase of the zinc content in the electric furnace dust, so how to realize high-value comprehensive utilization of zinc-containing dust has become a key issue for the sustainable development of the iron and steel industry. Based on the principle of recycling zinc resources, this study proposes an innovative full life cycle process for the preparation of electro-galvanized sheet using zinc-containing dust as raw material and deep eutectic solvents (DESs) as medium. Zinc plating was successfully prepared on duplex steel automobile plates using choline chloride-urea DES (Reline) as the electrolyte, and the modifying effects of different concentrations of nicotinic acid (NA) on the electrochemical system of the solution and the prepared plating were also investigated. The results show that the addition of 0.06 M NA to the system can modulate the deposition morphology of the plating layer, leading to the generation of ultrafine grains, and the surface densities and flatness are significantly improved. Besides, the addition of NA to the system increased the hardness value by 67.5 % and reduced the coefficient of friction by 38.8 % compared to the plating obtained in the system without additives; the corrosion rate decreased from 0.374 mm·a-1 to 0.023 mm·a-1, and the corrosion mechanism of the zinc layer was a direct dissolution process of zinc. This study realizes the morphology regulation of electroplated zinc plating, and greatly improves the aesthetics, mechanical properties and corrosion resistance of the plating at the same time, also the preparation process is in line with the requirements of the green development, which can provide a novel solution for the automotive high-quality plating.

Design and Implementation of An Automated Car Plate Number Recognition System (ACPNRS)

The escalating global volume of vehicles on roadways necessitates innovative solutions for efficient traffic management, heightened security, and improved law enforcement. This research project centers on developing an Automated Car Plate Recognition System (ACPRS) to address these challenges. The ACPRS utilizes cutting-edge technologies in image processing, machine learning, and database integration for accurate and real-time license plate recognition. Beginning with an exploration of existing methodologies and technologies in the field, the research highlights their strengths and limitations. The conceptualization phase involves meticulous design, incorporating image preprocessing, license plate detection, character segmentation, optical character recognition (OCR), and database interaction. The research emphasizes the utilization of advanced image processing techniques, including deep learning algorithms, for robust license plate detection. Spectral analysis and character segmentation ensure reliable recognition in challenging conditions. OCR techniques enhance alphanumeric character interpretation, contributing to overall efficacy. The implementation phase involves software development, database setup, and integration with mobile applications for enhanced accessibility. Real-world applicability and scalability are pivotal considerations. The ACPRS is deployable in various contexts, such as parking management, security systems, and access control. Compatibility with mobile applications ensures widespread accessibility, broadening potential applications in diverse settings. Security and privacy measures include user authentication and data encryption to safeguard sensitive information. To validate the ACPRS’s effectiveness, comprehensive testing and evaluation use a diverse dataset. Real-world images, synthetic data, and publicly available datasets assess performance under different conditions. Evaluation metrics such as accuracy, speed, and robustness quantify capabilities and identify areas for improvement. In conclusion, this research project represents a significant contribution to automated vehicle identification. The proposed ACPRS, focusing on accuracy, real-time processing, and mobile accessibility, addresses critical challenges in traffic management, security, and law enforcement. Thorough exploration, design, implementation, and evaluation phases ensure understanding of the system’s capabilities and potential real-world applications. This work will be very significant to the developing country in managing their traffic and other related issues.

Microcomb-Driven Optical Convolution for Car Plate Recognition

The great success of artificial intelligence (AI) calls for higher-performance computing accelerators, and optical neural networks (ONNs) with the advantages of high speed and low power consumption have become competitive candidates. However, most of the reported ONN architectures have demonstrated simple MNIST handwritten digit classification tasks due to relatively low precision. A microring resonator (MRR) weight bank can achieve a high-precision weight matrix and can increase computing density with the assistance of wavelength division multiplexing (WDM) technology offered by dissipative Kerr soliton (DKS) microcomb sources. Here, we implement a car plate recognition task based on an optical convolutional neural network (CNN). An integrated DKS microcomb was used to drive an MRR weight-bank-based photonic processor, and the computing precision of one optical convolution operation could reach 7 bits. The first convolutional layer was realized in the optical domain, and the remaining layers were performed in the electrical domain. Totally, the optoelectronic computing system (OCS) could achieve a comparable performance with a 64-bit digital computer for character classification. The error distribution obtained from the experiment was used to emulate the optical convolution operation of other layers. The probabilities of the softmax layer were slightly degraded, and the robustness of the CNN was reduced, but the recognition results were still acceptable. This work explores an MRR weight-bank-based OCS driven by a soliton microcomb to realize a real-life neural network task for the first time and provides a promising computational acceleration scheme for complex AI tasks.

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%.

A low cost IoT-based Arabic license plate recognition model for smart parking systems

License Plate Recognition is one of the significant enablers that can be utilized in wide range of applications in ITS and smart cities. The proposed design relies on three image processing stages to achieve license plate identification with high accuracy which are pre-processing, segmentation, and character recognition. The canny edge detection method with various thresholds, contour detection, and masking techniques are used to locate the car edges and license plate. In the experiment presented in this paper, 200 images were used to identify Egyptian car plates. The model successfully identified Arabic license plates with 93% accuracy. A prototype is implemented using ESP32 Cameras and Raspberry-Pi to test the system's performance. Moreover, a database and a website are hosted on the RPi to allow users to search for their car location in the parking lot using the car's full or partial license plate which was saved in database upon detection.

Vehicle Detection and Recognition for Allowing into Premises Using OCR

The research has proposed a system to automatically detect the number plate and recognize it using optical character recognition method. Before taking a image of the number plate, the developed system first recognizes the car. Image segmentation is used to recover the portion of an image that contains the vehicle identification number. Character recognition is accomplished using an optical character recognition technique. This involves using matching techniques to check whether the car plate image matches the data in the database. The warning sign will show when authenticity is verified, and the car will then be permitted to enter the designated area. Real-time video is captured to evaluate the system's functionality, and Python is used to create and simulate the system. Due to its promising nature the suggested method is employed in the automated vehicle authentication in universities in future.

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.

Juridical Review of Civilians Wearing Rfs Car Plates Reviewed by Law Number 22 Years 2009 on Traffic and Road Transport

This study aims to find out and analyze the regulations regarding the manufacture of "RFS" license plates on vehicles. As well as knowing and analyzing the legal consequences arising from making license plates on the roadside. This research belongs to the normative type of research. So it can be known that civil society using RFS car plates is a special license plate code ending in 'RFS' which is only given to certain circles and is not commonly used in the general public. The use of special license plate codes is regulated in the Regulation of the Chief of Police No. 3 of 2012 concerning the Issuance of Recommendations for Motor Vehicle Number Letters (STNK) and Special and Secret Motor Vehicle Number Signs (TNKB) for Official Motor Vehicles.

Detection and Recognition of Car Plates in Parking Lots at Baghdad University

A simple and smart algorithm was presented to recognize car plates in parking at the College of Science for Women, University of Baghdad, Iraq. The study consists of recording video clips of all cars parked in the selected area. The studied camera heights were1m and 2m, and the video clips were 19 and 30. Images were extracted from the video clip to be used for training data for the cascade method. Haar classification was used to detect license plates after the training step. Viola-jones algorithm was applied to the output of Haar’s data for both camera heights (1m and 2m). The accuracy was calculated for all data with different weather conditions and local time recoding. The accuracy is 100% for all data in this study.

Automatic Detection and Recognition of Car Plates Based on Cascade Classifier

The study consists of video clips of all cars parked in the selected area. The studied camera height is1.5 m, and the video clips are 18video clips. Images are extracted from the video clip to be used for training data for the cascade method. Cascade classification is used to detect license plates after the training step. Viola-jones algorithm was applied to the output of the cascade data for camera height (1.5m). The accuracy was calculated for all data with different weather conditions and local time recoding in two ways. The first used the detection of the car plate based on the video clip, and the accuracy was 100%. The second is using the clipped images stored in the positive file, based on the training file (XML file), where the accuracy was 99.8%.

Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

Every developing country relies on transportation, and there has been an exponential expansion in the development of various sorts of vehicles with various configurations, which is a major component strengthening the automobile sector. India is a developing country with increasing road traffic, which has resulted in challenges such as increased road accidents and traffic oversight issues. In the lack of a parametric technique for accurate vehicle recognition, which is a major worry in terms of reliability, high traffic density also leads to mayhem at checkpoints and toll plazas. A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues. The Automatic Licence Plate Recognition (ALPR) system is one of the components of the intelligent transportation system for traffic monitoring. This study is based on a comprehensive and detailed literature evaluation in the field of ALPR. The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate. The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition. The properties of characters are employed to recognise the Indian licence plate in this study. For licence plate recognition, more than 200 images were analysed with various parameters and soft computing techniques were applied. In comparison to neural networks, a hybrid technique using a Convolution Neural Network (CNN) and a Support Vector Machine (SVM) classifier has a 98.45% efficiency.

Analysis of loose roll mark of hot-rolled pickling alloyed galvanized automobile sheet

The loose coil mark of hot-rolled pickling alloyed galvanized automobile plate is one of the main defects affecting the quality of steel plates. In this paper, the composition of the loose coil mark and the coiling process parameters of the hot-rolled pickling galvanized sheet was analyzed. The results showed that the main reasons for the loose coil marks were insufficient friction between the coiler mandrel and the strip, and the coiling tension of the lead head was unstable. By increasing the lead rate of head coiling speed to 15 mpm and increasing the crimp tension limit, the loose coil marks of hot-rolled pickling alloyed galvanized automobile plate were significantly reduced, and the quality of the steel plate was significantly improved. The unqualified steel plate caused by the defect of plate loose coil marks was downgraded from 100 t/month to 15 t/month.

Development of resistance spot welding material card for dissimilar automobile plates

Taking the resistance spot welding of automotive steel DC04-HC420LA as an example, the failure forces including cross tensile, shear tensile and pull-out tensile tests were obtained in the test for dissimilar materials. The fracture failure model of resistance spot welding of dissimilar materials was established by the finite element analysis method. It was compared with the experimental data to verify the accuracy of the finite element analysis model of solder joint failure mode. And then the data of the welding material card was calibrated and verified. Finally, an accurate dissimilar welding material card was established, which can predict the solder joint failure of the key sensitive parts in the whole vehicle CAE collision analysis, and improve the accuracy of CAE collision analysis.

Design and Implementation of an Intelligent Parking System (ISPS) With Laser Security System (LSS)

      In order to solve one of the most important problems, especially in a large and crowded city like Baghdad, is finding an empty and secure car parking space.  In this work, an intelligent Smart Parking System (ISPS) has been investigated and developed by providing information about the empty lots and helping the driver to get to his parking spot with a rotary parking system to reduce the traffic in the parking area. The ISPS Park implements a car number plate recognition system as a security system for car identification. Moreover, for increased security, a fence with a Fiber-Optic vibration system has also been used to prevent any object from entering a parking space without a license. The ISPS was run with the pre-programmed controller to make minimum human involvement in the parking system to ensure access control in restricted places. Matlab with Arduino tools has been used for the car plate number recognition process and car entering control. The system shows that using RFID for entering and leaving is so fast and informative. A Fiber SenSys program has been used with the Alarm Processing Unit (APU) to prevent any unlicensed person from trying to cut, climb or crawl under a fence.

Car-Plate Image Enhancement by Using Median and Max Filters


 
 
 
 Nowadays, Optical Character recognition is one of the affected tools for recognition the plate of car from a still video or image in the intelligent transportation systems. The Optical Character Recognition accuracy incompletely depends on the input image quality. In this article, two efficient approaches are proposed to enhance the quality of the car plate image selected from video clips. To minimize the error rate the proposed technique is used even at low resolution image. Maximum, and median filters are used in this work to enhance the quality of image. These technique extend to collect the pixels of the consecutive frames of the video in filtering approaches. The error rate of the OCR is tested on fifty road and street video clips by decreasing the resolution of the images and filtering them with common and proposed filtering methods. The test results indicate that both proposed methods, improve the accuracy of OCR, and the highest reduction of error is obtained by the proposed temporal maximum filtering method.
 
 
 
 

Car license plate segmentation and recognition system based on deep learning

Artificial intelligence techniques and computer vision techniques dealt with the issue of automatic license plate recognition (ALPR) that has many applications in important research field. In this paper, the method of recognizing the license plates of Iraqi cars was applied based on deep learning techniques convolutional neural network (CNN). The two database built to identifying Iraqi car plates. First database includes 2000 images of Arabic numbers and Arabic letters. Second database conations 1200 images of the Arabic names for Iraqi governorates. This paper used image-processing techniques to segmenting the numbers, letters and words from the car license plate images and then convert them into two databases that used to train the two CNN. These training CNN used to recognizing the vocabulary of the car license plate. The success rate of the numbers, letters and words recognition was 98%. The overall rate of success of this proposed system in all stages was 97%.

Exam Marks Summation App Using Tesseract OCR in Python

There are no tools to auto grade subjective questions and no auto summations app to date. As a result, examiners need to mark subjective answer scripts manually and they might overlook some marks while summing up subjective answer scripts scores obtained by students. Therefore, this study aims to develop an exam mark summation app to ease miscalculation problems. Tesseract OCR was implemented in this Marks Summation App as a platform to scan the multi-page handwritten marks using pen tip above a 0.5mm size in red, blue, and black to recognise the marks wrote by the examiners. The captured marks images will be uploaded to server for image processing by promptly executing the python script in the server. The extraction of numbers from the processed image will sum up the scores to determine each candidate's final score. It is recommendable to scan the original materials to produce more accurate accuracy. Out of 118 numbers from 21 testing samples, 88 numbers are correctly recognised, whereas 30 are not recognised correctly. Thus, the accuracy of the developed exam mark summation app is 74.58%. The app's performance is more superior than car plate recognition result in the field. In the future, we hope to increase the app’s recognition accuracy by exploring deep learning in recognizing handwritten numbers.