License Plate Recognition Research Articles

Research on the Development Status of Highway Electronic Toll Collection System

Highway Electronic toll collection system(ETC) is the most important part of intelligent transport system. Research and analysis on the development status of ETC can provide a research basis for its future development. In this paper, three subsystems of the highway Electronic toll collection system (ETC): automatic vehicle identification, automatic vehicle classification, and audit system are studied, and by comparing the research methods of scholars in different periods of time on automatic vehicle identification, automatic vehicle model classification, and audit system, it is found that with the application of new technologies such as big data, edge computing, AI algorithms, and laser radar, the highway Electronic toll collection system (ETC) development level is increasing.

Tri-wavelength blending method for speckle reduction and color space enhancement in laser projection systems

A novel tri-wavelength blending speckle reduction method was proposed and studied both experimentally and theoretically. In the proposed method, a 543 nm diode pumped solid state (DPSS) laser was employed to cooperated with a shorter wavelength 523 nm laser diode (LD) and a common 532 nm DPSS laser. Comparing to the traditional speckle reduction method, the proposed method takes into account both speckle reduction and color space coverage of a project system. It was shown that speckle contrast ratio (SCR) as low as 3.3 % was achieved when the power ratio of 523 nm, 532 nm and 543 nm light were set to 4:1:1. The effect on the color gamut after the use of tri-wavelength blending method was also investigated. It was found that by using the proposed tri-wavelength blending green lasers together with the commercially available red (635 nm) and blue (467 nm) laser diodes, a color space coverage as large as 97.6 % of the Rec.2020 color gamut can be achieved.

Improving license plate recognition via diverse stylistic plate generation

Deep learning-based license plate recognition heavily relies on the quantity and diversity of training images. However, manually collecting diverse license plate images is a highly time-consuming and labor-intensive task. Hence, generating license plate images to supplement training data emerges as a cost-effective solution for recognition enhancement. Previous generation methods treated the style of license plates as discrete features and extracted style features from a referenced image to generate multi-style license plate images. However, these methods are incapable of generating license plates with arbitrary new styles, as the style of the generated images is constrained by the existing images. To solve this problem, we propose to extract style features from randomly sampled vectors in continuous space for diverse stylistic license plate generation. This way, license plates with arbitrary styles can be generated by simply modifying the sampled style vectors. Besides, to provide controllable content information for the generator, we propose an orientation-aware content encoder to extract multi-oriented glyph content features from the input horizontal glyphs. Moreover, a dual-discriminator is proposed to conduct adversarial learning on both color images and image gradients, aiming to mitigate unnatural patterns and ensure realistic generated results. Finally, extensive experiments on multiple public license plate datasets demonstrate that our method can generate realistic and diverse license plates, and the generated images can significantly improve recognition performance by up to 11.5%.

Parametric Production of Prostheses Using the Additive Polymer Manufacturing Technology Multi Jet Fusion.

This study aims to develop a procedure for the production of 3D-printed forearm prostheses (especially hard outer sockets). The production procedure is designed in the form of a parametric workflow (CAD model), which significantly speeds up the designing process of the prosthesis. This procedure is not fixedly dependent on the software (SW) equipment and is fully transferable into another SW environment. The use of these prostheses will significantly increase the comfort of their patients' lives. It is possible to produce prostheses faster and in larger amounts and variants by the usage of additive technology. The input for the own production of the prosthesis is a model of the internal soft socket of the patient. This soft socket (soft bed) is made by a qualified prosthetist. A 3D-scanned CAD model is obtained afterward using the scanning method by an automatic laser projector. An editable, parametric external socket (modifiable in any CAD format) is generated from the obtained 3D scan using a special algorithmic model. This socket, after the necessary individual modifications, is transferred to 3D printing technology and produced using powder technology Multi Jet Fusion (HP MJF). The result of the designed and tested procedure is a quickly editable 3D-printed outer socket (main part of prosthesis), which is able to fully replace the current long-fiber composite solution. Production of current solutions is relatively time-consuming, and only one piece is produced in a given time. The newly designed technology eliminates this. This study summarized the possibilities of speeding up the production of forearm prostheses (but not only these) by creating a parametric CAD model that is applicable to different patients.

ALP-Net: a segmentation-free approach for license plate recognition in unconstrained scenarios

ALP-Net: a segmentation-free approach for license plate recognition in unconstrained scenarios

Stochastic queue profile estimation using license plate recognition data

Queue profile has garnered significant attention for its precise description of queue formation and dissipation at signalized intersections. License Plate Recognition (LPR) systems, recording full-sample lane-based departure times of individual vehicles, offer an ideal data source for queue profile estimation. Thus, this paper presents a stochastic queue profile estimation method utilizing LPR data. It adopts the classic Input-Output model as a baseline to reconstruct arrival and departure curves using departure information from LPR detectors at successive intersections. A pseudo departure curve is then derived from the distribution of free-flow travel time. The stochastic queue profile is further reconstructed by analyzing the relationship among these curves, accounting for potential lane changes and overtaking behavior. The proposed method is validated through empirical and simulation cases. The empirical case achieves accurate queue length estimation with a Mean Absolute Error (MAE) of 5.38 m, while the simulation case yields precise stochastic queue profiles with an MAE of 5.95 m and an average deviation of 2.40 m for queue length. Sensitivity analysis demonstrates the method's robustness across parameters like demand-to-capacity ratio, miss detection ratio, and overtaking ratio. This work holds promise for real-time signal control optimization and vehicular trajectory reconstruction.

Efficient Helmet and Number Plate Detection using YOLOv3: A Road Safety Perspective

Abstract: Object recognition is an important aspect of machine learning and CV tasks, in addition to many real-world uses, including traffic welfare. In this work, we provide a YOLOv3-based trained model for number plate and helmet recognition in photos. Pre-processing the data improved its quality and diversity. We gathered a dataset of annotated photos of helmets and licence plates. We trained our model with a multi-phase program for learning rates using the dataset, then we assessed its efficacy on an independent test collection set. Additionally, we examined its advantages along with disadvantages of the methodology we used also spoken about potential developments that can be done in coming times to raise the model's precision and effectiveness. Our study advances the development of realistic, efficient, along systems for detecting objects that increase traffic welfare.

Helmet Detection And Number Plate Recognition Using YOLOV5

Helmet Detection And Number Plate Recognition Using YOLOV5

Automated Parking Lot Management System

Automated parking management system, utilizing machine learning for license plate recognition, has simplified the ticket generation and parking space allocation process in large parking areas. It offers a user-friendly experience by swiftly recognizing license plates upon vehicle entry. Furthermore, the system efficiently guides vehicles to the most suitable parking spaces, optimizing usage and alleviating congestion. This innovative approach streamlines these procedures, enhancing user convenience. It also plays a part in creating a more sustainable urban environment by reducing fuel consumption and emissions. To sum it up, our integration of machine learning for license plate recognition provides an efficient and eco-conscious solution to urban parking challenges, making parking hassle-free and environmentally friendly

Enhanced Surveillance: Developing a Computer Vision-based CCTV Footage Analysis Tool for Improved Vehicle Number Plate Recognition

Abstract: Smart parking systems serve as integral components in augmenting the efficiency and sustainability of modern urban environments. Nonetheless, prevailing systems predominantly rely on sensors to monitor parking space occupancy, entailing substantial installation and maintenance costs while offering limited functionality in tracking vehicle movement within parking lots. To confront these challenges head-on, our solution introduces a multistage learning-based approach, capitalizing on the existing surveillance infrastructure within parking facilities and a meticulously curated dataset of Saudi license plates. Our approach amalgamates YOLOv5 for license plate detection, YOLOv8 for character detection, and a novel convolutional neural network architecture tailored for enhanced character recognition. We illustrate the superiority of our approach over single-stage methods, achieving an impressive overall accuracy of 96.1%, a notable advancement from the 83.9% accuracy of single-stage approaches. Furthermore, our solution seamlessly integrates into a web-based dashboard, facilitating real-time visualization and statistical analysis of parking space occupancy and vehicle movement, all accomplished with commendable time efficiency. This endeavor underscores the potential of leveraging existing technological resources to bolster the operational efficiency and environmental sustainability of smart cities. By harnessing the power of machine learning and computer vision, our work exemplifies a paradigm shift towards smarter and more adaptive urban infrastructure

Automatic Number Plate Recognition

Automatic license plate recognition (ANPR) systems have become suitable for various applications, including traffic monitoring, law enforcement, and toll collection. This paper completes the study on automatic license plate recognition (ANPR) systems that use advanced imaging technology and machine learning algorithms to achieve accuracy in license plate verification and validation. The preparation process is adopted in various ways: image acquisition, preprocessing, location plate, character segmentation, and optical character recognition (OCR). The system, which integrates deep learning models for extraction and classification, performs better in different environments. Experimental results show that the identification accuracy of the proposed ANPR exceeds 95%, demonstrating its potential in practical applications. In addition, this paper also discusses the problems encountered in ANPR implementation, including changes in plate design, illumination, and shading, and provides suggestions for future research to improve stability and efficiency. Keywords: automatic plate recognition, image processing, machine learning, optical behavior recognition, deep learning, vehicle tracking, driving license inspection.

Real-time number plate detection using AI and ML

The abstract presents a research study focusing on real-time license plate verification, a key feature of electronic systems that operate by rapidly identifying and removing identification numbers from vehicle registration in a dynamic global environment. The research leverages the combination of artificial intelligence (AI) and machine learning (ML) techniques, specifically the integration of region-based convolutional neural networks (RCNN) and advanced RCNN algorithms, to create a powerful and readily available system. In terms of methods, this research optimizes algorithm performance and deploys the system in a cloud-based environment to improve accessibility and scalability. Through careful design and optimization, the proposed system has achieved a consistent result in license recognition, as evident from the well-accounted evaluation of performance, including precision, recall, and computational efficiency. The results demonstrate the efficiency and usability of this system in a real installation and promise to revolutionize automatic vehicle identification. Finally, the integration of artificial intelligence and machine learning technology into real-time license plate recognition signifies changes in traffic management, assessment safety and smart city plans. Therefore, interdisciplinary collaboration and continuous innovation are crucial to shaping a sustainable and balanced future for intelligent transportation systems.

Real Time Object Detection System Using Yolov

Improving object detection models in computer vision is a crucial area of focus. Object detection represents a more sophisticated approach to image classification, where a neural network identifies objects within an image and delineates them using bounding boxes. YOLO (You Only Look Once) revolutionized object detection by introducing an end-to-end neural network architecture that simultaneously predicts bounding boxes and class probabilities. Unlike earlier methods that adapted classifiers for detection, YOLO's approach streamlines the process by integrating classification and localization tasks, accurately delineating objects with bounding boxes. The project mainly focuses on augmenting YOLOv4, a state-of-the-art object detection architecture, to address the limitations like small object detection, adaptive lighting handling, occlusion challenges, detecting objects at different angles, and specialized object features. Additionally, we aim to improve the model's ability to recognize specialized object features for superior classification. Furthermore, by incorporating functionalities like license plate recognition using Tesseract OCR, object counting (total and class-wise), and detection cropping, this project extends the model's applicability to real-world scenarios. These functionalities make the model valuable in areas like autonomous vehicles, traffic monitoring, and industrial automation. The augmented model's improvements aim to overcome drawbacks identified in existing research, enhancing its efficacy in various scenarios.

A Real-Time License Plate Detection and Recognition Model in Unconstrained Scenarios.

Accurate and fast recognition of vehicle license plates from natural scene images is a crucial and challenging task. Existing methods can recognize license plates in simple scenarios, but their performance degrades significantly in complex environments. A novel license plate detection and recognition model YOLOv5-PDLPR is proposed, which employs YOLOv5 target detection algorithm in the license plate detection part and uses the PDLPR algorithm proposed in this paper in the license plate recognition part. The PDLPR algorithm is mainly designed as follows: (1) A Multi-Head Attention mechanism is used to accurately recognize individual characters. (2) A global feature extractor network is designed to improve the completeness of the network for feature extraction. (3) The latest parallel decoder architecture is adopted to improve the inference efficiency. The experimental results show that the proposed algorithm has better accuracy and speed than the comparison algorithms, can achieve real-time recognition, and has high efficiency and robustness in complex scenes.

Cross Stage Partial Dilated Convolution Network for License Plate Recognition

Cross Stage Partial Dilated Convolution Network for License Plate Recognition

Holistic image‐based analysis of damage on concrete surfaces—A multifaceted approach based on supervised machine learning

AbstractTraditional manual methods for inspecting damage on building structures, such as cracks or spalling on concrete surfaces, are laborious, costly, and error‐prone. Despite many attempts to automate this task using digital photographs, most studies primarily focus on detecting damage within images, neglecting the actual dimensions and their implications for structural integrity. To bridge this gap, we present a multifaceted approach for holistic damage analysis that not only detects damage within images but also determines its real‐world dimensions. To achieve this, we first distinguish between linear and areal damage, and apply two separate methods based on deep learning, each tailored to detect these specific types of damage within images. Additionally, we use a cost‐effective 3D‐printed laser projection device to project a grid of laser points onto the surface. This grid, with a known and fixed point‐to‐point distance, serves as a scale reference, facilitating true‐to‐scale measurements of the damage area. Furthermore, for depth estimation of areal damage, we employ models for monocular depth prediction trained in domains distinct from ours. We thoroughly evaluated our methods on realistic and challenging image datasets, which we captured ourselves in public space. The results show that our customized methods for damage detection achieved moderate results for linear damage and more promising results for areal damage. The quantification of damage area resulted in errors less than 10% across all evaluated images, which is suitable for most practical applications. However, estimating the depth of areal damage using models trained on distinct domains proved to be a challenge. Our research expands automated damage detection to include comprehensive, true‐to‐scale analysis of damage and underscores the need for continued refinement.

A novel method for indian vehicle registration number plate detection and recognition using CNN

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. Computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as “extraction,” “pre-processing,” and “segmentation,” and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard.

Aluminium and praseodymium doped M-type hexaferrites for electric vehicle applications

The booming demand for electric vehicles, driven by the pursuit of enhanced efficiency, faces significant hurdles attributed to the costly integration of rare earth magnets like NdFeB. M-type strontium ferrite emerges as a promising alternative owing to its economic viability and exceptional magnetic characteristics, rendering it suitable for electric vehicle motor applications. This research focuses on the synthesis and characterization of SrFe12O19 and Sr1-xPrxFe10Al2O19 (where x = 0.00 to 0.20) hexaferrites, with praseodymium (Pr) substitution. Magnetic particles were synthesized utilizing the sol-gel auto-combustion technique, employing a calcination temperature of 1100 °C for a duration of 4 h. Structural and magnetic studies of aluminium-praseodymium (Al–Pr) doped samples are performed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and vibrating-sample magnetometry (VSM). The structural analysis reveals a decrease in both particle size from 42 nm to 31.76 nm and lattice parameter with increasing doping concentration. Magnetic analysis yields crucial insights: Al3+ doping induces a substantial rise in coercivity from 5500 Oe to 10,500 Oe which is quite comparable to NdFeB, while Pr doping shows a subtle impact, enhancing remanence from 17 emu/g to 23.05 emu/g. These findings underscore the potential for optimizing magnetic properties through meticulous control of dopant concentrations in M-type hexaferrites, thereby positioning them as promising substitutes for rare earth magnets.

Automatic Traffic E-challan Generation

Utilizing state-of-the-art technology, Automated Traffic E-challan Generation systems are employed to promptly detect and document traffic violations as they occur. By seamlessly integrating cameras, sensors, and sophisticated algorithms, these systems independently identify transgressions including but not limited to speeding and unauthorized parking. After detection, electronic citations (e-challans) are swiftly produced and delivered to vehicle proprietors, thereby optimizing enforcement procedures and bolstering overall road safety. However, notwithstanding their efficacy, the deployment of such systems mandates the meticulous consideration of privacy and equity issues, necessitating the enactment of suitable regulatory protocols. The Automatic recognition of license plate is the basis of effective management in traffic, the automatic detection and localization of license plate is an important part. Keywords- Python, Raspberry Pi, Convolutional Neural Network

StreetSavvy: Automated Parking Management System with License Plate Recognition and Pricing

The Parking Charge Calculator represents a meticulously designed system aimed at accurately determining the parking charges for vehicles within a parking facility. This innovative solution leverages Automatic Number Plate Recognition (ANPR) technology in conjunction with a robust database system to streamline the process of identifying vehicles and computing corresponding parking fees. Within the confines of a parking lot, the unique identification of vehicles is facilitated through the extraction of information from license plates. When a vehicle enters the parking lot, the Parking Charge Calculator initiates the retrieval of the license plate number through the ANPR technology. This automated recognition system efficiently captures and records the alphanumeric sequence on the license plate, laying the foundation for subsequent charge calculations. The fundamental mechanism employed by the Parking Charge Calculator involves the multiplication of hourly parking charges by the temporal duration of a vehicle's stay. As the vehicle enters the parking lot, the system records the entry time. Upon the vehicle's departure, the departure time is similarly recorded. The time difference between these two instances becomes a critical parameter in the calculation process. Hourly parking charges, predetermined and configured within the system, serve as the unit of measurement for the calculation. The Parking Charge Calculator multiplies these charges by the elapsed time, providing a precise and automated method for determining the parking fee for a given vehicle. This approach not only ensures accuracy in charge computation but also eliminates the need for manual intervention, promoting efficiency and reducing potential errors. The integration of ANPR technology and a sophisticated database system not only expedites the identification process but also contributes to the overall reliability and security of the Parking Charge Calculator. By automating the parking charge calculation based on vehicle entry and exit times, this system offers a seamless and transparent method for both parking lot operators and vehicle owners, enhancing the overall parking experience and fostering a more technologically advanced approach to parking Management