License Plate Recognition Research Articles

LICENSE PLATE DETECTION USING MACHINE LEARNING

A crucial technology with many applications in traffic management, security, and law enforcement is license plate detection utilizing machine learning. In this abstract, a machine learning-based method for precisely locating and identifying license plates in pictures and video streams is presented. Data gathering, preprocessing, object detection using deep learning methods like YOLO, license plate recognition using optical character recognition (OCR), and post-processing to improve findings are all part of the mPlate Detection, OpenCV module, OCR Technology.ethodology. Metrics including precision, recall, F1-score, are used to assess performance. The system offers enormous promise for improving efficiency and safety in numerous sectors when it is implemented in real-world circumstances, whether on edge devices or in the cloud. This abstract emphasizes how important machine learning is for automating difficult processes, solving real-world problems, and improving CV. The trained model can also be used in a variety of applications, including security systems, toll booth monitoring, parking enforcement, traffic management systems, and parking enforcement. The system can be implemented on edge devices for real-time processing or in the cloud for scalability depending on the individual requirements of the application.

Research progress of laser lithography

Lithography is one of the key technologies that restrict the development of the semiconductor industry and its important role continues to be highlighted. This paper will review laser projection lithography and laser maskless lithography based on the theoretical knowledge of laser lithography, present the advantages of laser maskless lithography, discuss the latest progress of laser lithography in application fields, explore its development prospects and trends, and provide some ideas and inspiration for the further development of human laser lithography. The result shows that laser lithography can be used in the field of material processing, and lithography is one of the key technologies to fabricate semiconductor devices. The world’s most advanced lithography machine is the EUV lithography machine of the Dutch ASML. This EUV lithography machine can be used for the production of 5nm chips, so 5nm is the most advanced chip manufacturing process that EUV lithography machine can achieve. Since lithography is a high degree of composite technology, each component plays an irreplaceable role. Therefore, if further improvements are to be made to the chip process, researchers should look at all parts of the exposure system, photoresist and process technology to make them work together in harmony.

Objective tools in pediatric voice therapy: A path to improved outcomes

This is an invited presentation in the Special Session titled, “Voice therapy: objective measurement science and clinical efficacy.” Pediatric dysphonia (hoarseness) is a common condition with prevalence estimates ranging from 1.4% to 23.9%. Dysphonia can be detrimental to children both psychologically and academically; hence, early identification and restoration of optimal vocal health is critical. Direct visualization of vocal fold vibratory patterns is fundamental for appropriate diagnosis and treatment of vocal fold pathology. Thus, evaluation of vocal fold structure and cycle-to-cycle vibratory motion through techniques of direct visualization of the vocal fold is essential for clinical assessment and measurement of treatment outcomes. This session will discuss the most recent advances in objective voice assessment and measurement of voice therapy outcomes in children. The focus will be on discussing an innovative approach that leverages high-speed videoendoscopy and a cutting-edge custom developed laser projection system for quantitative evaluation of lesion size and comprehensive evaluation of opening and closing phase dynamics of the glottal cycle before and after voice therapy in children with vocal fold nodules.

Laser speckle reduction using polymer-stabilized liquid crystals doped with Ag nanowires

Miniaturized and pure static devices are expected to be used in laser-imaging systems for speckle reduction. In this study, a pure static device based on polymer-stabilized liquid crystal (PSLC) doped with Ag nanowires was developed to effectively suppress laser speckles. The concentrations of the polymer and Ag nanowires in the PSLC were optimized, and then the PSLC devices were fabricated. A measurement system was set up to characterize the electro-optical properties of the fabricated PSLC devices. Subsequently, a laser projection system was built to demonstrate the speckle-reduction performance. Moreover, the degree of scattering and response time of the developed PSLC devices were investigated and discussed. A PSLC doped with 0.02 wt% Ag nanowires and 3 wt% polymer having a device size of 2 × 2 × 0.1 cm3 was demonstrated to produce a speckle-reduction efficiency of 51.4 % under very low driving voltages. The experimental results verified effectiveness and superiority of the developed speckle reduction method based on PSLC doped with Ag nanowires.

Clinical evaluation of a fluoroscopic image-based laser guidance system in bone tumor surgery: A technical note.

This study presents a laser guidance system developed to enhance surgical accuracy and reduce radiation exposure in orthopedic surgeries. The system can project the actual position corresponding to the appointed position selected by the surgeon on a fluoroscopic image using a line laser and has laser projection ability to mark the corresponding point using a line laser. The surgeon does not have to perform anatomical marker placement for calibration. Three patients with bone tumors underwent surgeries using the laser guidance system, and the projection accuracy was evaluated by measuring the distance error between the appointed and laser-marking positions. The installation time, including calibration, was also assessed for clinical usability. The average projection accuracy in bone tumor surgery was 2.86mm, and the average installation time was 7min. These results demonstrate that the laser guidance system, with a projection error of <3mm, could be useful in bone tumor surgeries.

Comparison Study About Vehicle Detection and Plate Number Recognition

Vehicle detection is crucial to the management of Intelligent Transportation Systems of urban traffic. Optical character recognition is many systems use, like the automatic number plate recognition system (NPR). Using various techniques and methodologies, such as convolution or deep neural networks, morphological operations, optical character recognition, and edge detection, such a system has been widely utilized to identify car license plates. Numerous applications algorithms of vehicle identification become possible after the development of many helpful techniques in computer vision and the availability number of related images as a dataset. In this paper, the various plate numbers of the specific vehicle will be and reviewed. Vehicle detection systems suffer from a variety of difficulties and constraints such as the all the surrounding state of clarity for the license plate, non-standard formats of the plate, complex situations, camera quality and clarity, the position and distance of the camera, the distortion in the image, contrast issues, reflections and the state day and night images As a result, numerous methods for comprehending and assessing the idea behind the vehicle number plate recognition systems have been presented with simply explaining the importance and drawbacks of each system.

Automatic Jordanian License Plate Detection and Recognition System Using Deep Learning Techniques.

Recently, the number of vehicles on the road, especially in urban centres, has increased dramatically due to the increasing trend of individuals towards urbanisation. As a result, manual detection and recognition of vehicles (i.e., license plates and vehicle manufacturers) become an arduous task and beyond human capabilities. In this paper, we have developed a system using transfer learning-based deep learning (DL) techniques to identify Jordanian vehicles automatically. The YOLOv3 (You Only Look Once) model was re-trained using transfer learning to accomplish license plate detection, character recognition, and vehicle logo detection. In contrast, the VGG16 (Visual Geometry Group) model was re-trained to accomplish the vehicle logo recognition. To train and test these models, four datasets have been collected. The first dataset consists of 7035 Jordanian vehicle images, the second dataset consists of 7176 Jordanian license plates, and the third dataset consists of 8271 Jordanian vehicle images. These datasets have been used to train and test the YOLOv3 model for Jordanian license plate detection, character recognition, and vehicle logo detection. In comparison, the fourth dataset consists of 158,230 vehicle logo images used to train and test the VGG16 model for vehicle logo recognition. Text measures were used to evaluate the performance of our developed system. Moreover, the mean average precision (mAP) measure was used to assess the YOLOv3 model of the detection tasks (i.e., license plate detection and vehicle logo detection). For license plate detection, the precision, recall, F-measure, and mAP were 99.6%, 100%, 99.8%, and 99.9%, respectively. While for character recognition, the precision, recall, and F-measure were 100%, 99.9%, and 99.95%, respectively. The performance of the license plate recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 99.8%, 99.8%, and 99.8%, respectively. Furthermore, for vehicle logo detection, the precision, recall, F-measure, and mAP were 99%, 99.6%, 99.3%, and 99.1%, respectively, while for vehicle logo recognition, the precision, recall, and F-measure were 98%, 98%, and 98%, respectively. The performance of the vehicle logo recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 95.3%, 99.5%, and 97.4%, respectively.

A Design Space Exploration of Power-efficient Gaussian Filter Architectures using Logical Optimization and Approximated Adders

Gaussian filtering is an important step in many image-processing applications because it reduces image noise.However, this step is also compute-intensive, so power-optimized hardware architectures are necessary to allow its adoption in embedded devices. This work presents a design space exploration of power-efficient Gaussian filter architectures.Differently from related work, this work shows the impacts of the design decisions on two target applications: the Canny Algorithm and an Automatic License Plate Recognition system.To explore the design space, the 3x3, 5x5, and 7x7kernels were logically refactored using Multiplierless Constant Multiplication and Common Sub-expression Exploration. The adders were approximated using the copy strategy to further reduce power consumption. Systematic experiments show the effects of the adopted strategies on power savings and quality of results compared to exact baselines using the two applications.The approximate strategy reached up to 51 dB of Peak Signal-to-Noise Ratio with power reductions of up to 48% in the best-case scenario of the standalone Gaussian filters. Also, the total power of the Gaussian filter in the Canny Algorithm can be reduced down to 34% while maintaining the precision of results between 57% and 90%. Finally, the proposed strategies reduce up to 64% of the Gaussian filter power consumption when adopted in the plate detection solution with a similar detection rate compared to the exact filter architecture.

Generates Warning Signals Based on Number Plate Recognition to Prevent Parking Violations

Parking violations are very frequent around us, especially in urban areas, which greatly disturbs other road users so the government imposes strict sanctions on parking violations. To solve this problem, a surveillance system is needed to immediately alert drivers to move their vehicles to the correct parking space. In this paper, a monitoring system for parking violations has been developed that focuses on carrying out license plate recognition on cars and giving warnings to drivers to move their cars from the wrong parking location via voice media. The steps involved in license plate recognition are license plate detection, character segmentation, and character recognition by performing OCR (Optical Character Recognition). Furthermore, the license plate recognition results will be used as input for sound generation as a warning. In this paper, a GUI was also developed to help users run programs that have been developed. Finally, the experimental results show that a simple monitoring system to alert parking violations can be established.

Achromatic diffractive liquid-crystal optics for virtual reality displays

Diffractive liquid-crystal optics is a promising optical element for virtual reality (VR) and mixed reality as it provides an ultrathin formfactor and lightweight for human factors and ergonomics. However, its severe chromatic aberrations impose a big challenge for full-color display applications. In this study, we demonstrate an achromatic diffractive liquid-crystal device to overcome this longstanding chromatic aberration issue. The proposed device consists of three stacked diffractive liquid crystal optical elements with specifically designed spectral response and polarization selectivity. The concept is validated by both simulations and experiments. Our experimental results show a significant improvement in imaging performance with two types of light engines: a laser projector and an organic light-emitting diode display panel. In addition, our simulation results indicate that the lateral color shift is reduced by ~100 times in comparison with conventional broadband diffractive liquid-crystal lens. Potential applications for VR-enabled metaverse, spatial computing, and digital twins that have found widespread applications in smart tourism, smart education, smart healthcare, smart manufacturing, and smart construction are foreseeable.

Multi-attribute fusion-based approach for Algerian automatic license plate recognition

Multi-attribute fusion-based approach for Algerian automatic license plate recognition

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

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.

LICENSE PLATE RECOGNITION SYSTEM BASED ON MASK R-CNN

Automatic license plate recognition (ALPR) systems can be found in many different applications. If a person has a driving license, the person probably had already seen smart road camera after a speed limit sign or on a crossroad. Every year number of cars on roads growth very fast. It is also obvious that such systems can be used out-of-road situations. For instance, this type of systems can be used for automatic access control on private property or smart parking, or even log system that are being used literally everywhere. Because of popularity of ALPR systems, there are two main goals, which are being pursued by researches: speed and accuracy of recognition. Speed of the detection is important for real-time systems. Accuracy is important for every system. The more accurate a system is, the more reliable it is. For example, car accident detection systems should be as accurate as possible in order to be used, because no one wants to get billed with the wrongdoing, that wasn’t committed by them.&#x0D; The purpose of the study is to develop high precision automatic license plate detection system with number extraction possibilities. In order to achieve the goal many different modern solutions and technologies were studied and solution is presented. The main technology of the project is artificial intelligence system and, more specifically, convolutional neural network. As the main algorithm Mask R-CNN is used for license plate detection. To present reasonable research, the system was tested on different hardware (CPU, GPU, Raspberry PI 4) and on different datasets.

Feature Point Identification in Fillet Weld Joints Using an Improved CPDA Method

An intelligent, vision-guided welding robot is highly desired in machinery manufacturing, the ship industry, and vehicle engineering. The performance of the system greatly depends on the effective identification of weld seam features and the three-dimensional (3D) reconstruction of the weld seam position in a complex industrial environment. In this paper, a 3D visual sensing system with a structured laser projector and CCD camera is developed to obtain the geometry information of fillet weld seams in robot welding. By accounting for the inclination characteristics of the laser stripe in fillet welding, a Gaussian-weighted PCA-based laser center line extraction method is proposed. Smoother laser centerlines can be obtained at large, inclined angles. Furthermore, an improved chord-to-point distance accumulation (CPDA) method with polygon approximation is proposed to identify the feature corner location in center line images. The proposed method is validated numerically with simulated piece-wise linear laser stripes and experimentally with automated robot welding. By comparing this method with the grayscale gravity method, Hessian-matrix-based method, and conventional CPDA method, the proposed improved CPDA method with PCA center extraction is shown to have high accuracy and robustness in noisy welding environments. The proposed method meets the need for vision-aided automated welding robots by achieving greater than 95% accuracy in corner feature point identification in fillet welding.

Projection mapping system for laser speckle contrast image: feasibility study for clinical application.

Laser speckle contrast images (LSCIs) have been utilized to monitor blood flow perfusion. However, they have conventionally been observed on monitor screens, resulting in potential spatial mismatching between the imaging region of interest (IROI) and monitor screen. This study proposes a projection mapping (PM) system for LSCIs (PMS_LSCI) that projects LSCIs to directly observe the blood flow perfusion in the IROI. The PMS_LSCI consists of a camera, imaging optics, a laser projector, and graphic user interface software. The spatial matching in the regions of interest was performed by adjusting the software screen of the LSCI in the IROI and evaluated by conducting in-vitro and in-vivo studies. An additional in-vivo study was performed to investigate the feasibility of real-time PM of the LSCI. The spatial mismatching in the regions of interest was ranged from 2.74% to 6.47% depending on the surface curvature. The PMS_LSCI could enable real-time PM of LSCI at four different blood flow states depending on blood pressure. The PMS_LSCI projects the LSCI in the IROI by interacting with a projector instead of the monitor screen. The PMS_LSCI presented clinical feasibility in the in-vitro and in-vivo studies.

Number Plate Detection in an Image

Automatic Vehicle license plate detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for license plate detection and recognitions. Due to the varying characteristics of the license plate like numbering system, colors, style and sizes of license plate, When detection and recognition are two separate jobs, which also results in a huge number of factors, there is an issue with identification. So,further research is still needed in this area. We propose a unified convolutional neural network (CNN) and the F1 score as metrics in a deep learning project for picture categorization which can localize license plates and recognize the letters. We work on license plate recognition and segments characters in the license plate firstly, and then recognizes each segmented character using Optical Character Recognition(OCR)techniques. Extensive experiments show the effectiveness and the efficiency of our proposed approach.

Benchmarking Probabilistic Deep Learning Methods for License Plate Recognition

Learning-based algorithms for automated license plate recognition implicitly assume that the training and test data are well aligned. However, this may not be the case under extreme environmental conditions, or in forensic applications where the system cannot be trained for a specific acquisition device. Predictions on such out-of-distribution images have an increased chance of failing. But this failure case is oftentimes hard to recognize for a human operator or an automated system. Hence, in this work we propose to model the prediction uncertainty for license plate recognition explicitly. Such an uncertainty measure allows to detect false predictions, indicating an analyst when not to trust the result of the automated license plate recognition. In this paper, we compare three methods for uncertainty quantification on two architectures. The experiments on synthetic noisy or blurred low-resolution images show that the predictive uncertainty reliably finds wrong predictions. We also show that a multi-task combination of classification and super-resolution improves the recognition performance by 109% and the detection of wrong predictions by 29%.

Optimizing Systemic Redundancy of Traffic Sensor Networks While Maintaining Resilience: New Evidence From Using Graph Learning

The optimization of systemic redundancy by minimizing the sensor quantity can improve the efficiency of sensor networks and save costs. However, from the perspective of risk management, this redundancy reduction can also bring a significant loss in the overall network resilience because the less the systemic redundancy is, the fewer backup components in the network when shocks hit and, therefore, the less overall resilience. In this article, we investigate this intractable dilemma and attempt to pinpoint the tradeoff point for a city-scale automatic number plate recognition (ANPR) system in Cambridge, U.K. By developing a two-stage graph deep learning model, we first optimize the layout of the ANPR system to reduce redundancy and find its efficiency profile. Next, we study what effects this redundancy reduction can bring to the overall resilience, as the overall observability drops with the reduction in the number of sensors and find an optimal balance. The results show that our approach can effectively optimize the system's redundancy by using only 47% of the original sensors to reconstruct the full picture with a mean absolute error of only 11.18 and a root mean square error of 19.49; most importantly, the overall system resilience is maintained at 70% in the meantime. This article provides an alternative perspective for dealing with the well-known “efficiency-resilience” dilemma and offers new evidence to enable better decision and policy making for city managers and planners in local authorities.

Traffic arrival pattern estimation at urban intersection using license plate recognition data

Traffic arrival pattern is an indispensable part of the traffic state analysis and optimization of signal control schemes at the urban intersection. In some previous research relevant to the traffic parameters at the intersection, such as the queue length estimation, delay estimation, and vehicle trajectory reconstruction, the vehicle arrival patterns are assumed to obey a Normal or Poisson distribution. However, the actual traffic arrival patterns at the intersection are dynamic and more complex. License plate recognition (LPR) data containing rich vehicle trajectory information are promising in data-driven applications of traffic control and management. We propose a framework based on the LPR data, to infer the dynamic traffic arrival pattern at the intersection. The proposed integrative data-driven framework consists of three main parts, a method for inferring traffic signal cycle parameters, deriving the cycle start time and cycle length from time headway sequences; a link travel time estimation method, extracting the valid travel time at the selected time interval, and an arrival pattern estimation model based on the above modules to perform the second-level estimation at the downstream intersection. The unique characteristics of LPR data are exploited and utilized in this study. To validate the proposed method, the LPR data from two adjacent intersections in the city of Changsha in China are used. Numerical results reveal that the proposed framework can achieve satisfactory estimation under different traffic scenarios. The findings in this study can be extended for supporting efficient traffic control applications.

MVSR Normalization Algorithm Method for Improving Vehicle License Plate Recognition

Image processing and embedded systems are used in many applications such as object recognition, robotic applications, autonomous and remote control systems developed for the defense industry, medical applications, face recognition, and vehicle license plate recognition. Many vehicle license number plate detection methods are not effective under vehicle license plate images have a degree of rotation or low resolution images. Thus, we used MVSR normalization algorithm to detect vehicle license plate recognition for better accuracy and lower computational cost. The MVSR normalization algorithm, Mean–Variance-Softmax-Rescale processes respectively is applied for high-accuracy real-time vehicle license plate detection.