License Plate Recognition Research Articles

Calibration method of a three-dimensional scanner based on a line laser projector and a camera with 1-axis rotating mechanism

Calibration method of a three-dimensional scanner based on a line laser projector and a camera with 1-axis rotating mechanism

Automatic Parking Management System and Parking Fee Collection Based on Number Plate Recognition

This research paper dives into the complexities of a programmed stopping framework and electronic stopping expense assortment, principally founded on vehicle number plate acknowledgment. The essential goal is to plan and carry out a programmed stopping framework that essentially improves the accommodation and security of public parking areas. Additionally, it aims to facilitate parking fee collection without the complexities associated with magnetic cards. The proposed automatic parking system boasts several key features. First and foremost, it minimizes human interaction by eliminating the need for magnetic cards and associated devices. Users can seamlessly enter and exit parking lots without physical tokens, enhancing overall convenience. Furthermore, the system incorporates a parking guidance feature that directs users to available parking spaces. Real-time information about vacant spots ensures efficient utilization of parking areas. The heart of the system lies in its number plate recognition capabilities. The significance of this research extends beyond parking management. It contributes to ongoing efforts to automate and digitize essential services, making them more accessible and user-friendly. By embracing such technological advancements, cities can enhance user experiences, improve efficiency, and pave the way for a future of automated systems in public services.

Research on the Car Searching System in the Multi-Storey Garage with the RSSI Indoor Locating Based on Neural Network

To solve the problem of reverse car searching in intelligent multi-story garages or parking lots, the reverse car searching method based on the intelligent garage of the PC client and mobile client APP was studied, and the interface design and function development of the system’s PC and mobile client APP were carried out. YOLOv5 network and LPRNet network were used for license plate location and recognition to realize parking and entry detection. The indoor pedestrian location method based on RSSI fingerprint signal fusion BPNet network and KNN algorithm was studied, and the location accuracy within 2.5 m was found to be 100%. The research on the A* algorithm based on spatial accessibility was conducted to realize the reverse car search function. The research results indicate that the guidance of the vehicle finding path can be completed while the number of invalid search nodes for the example maps was reduced by more than 55.0%, and the operating efficiency of the algorithm increased to 28.5%.

Improving Data Augmentation for YOLOv5 Using Enhanced Segment Anything Model

As one of the state-of-the-art object detection algorithms, YOLOv5 relies heavily on the quality of the training dataset. In order to improve the detection accuracy and performance of YOLOv5 and to reduce its false positive and false negative rates, we propose to improve the Segment Anything Model (SAM) used for data augmentation. The feature maps and mask predictions generated by the SAM are used as auxiliary inputs for the Mask-to-Mask (M2M) module. The experimental results show that after processing the dataset with the improved Segment Anything Model, the detection performance of YOLOv5 is improved with 99.9% precision and 99.1% recall. The improved YOLOv5 model has a higher license plate recognition accuracy than the original detection model under strong snowfall conditions, and the incidence of false-negative and false-positive is greatly reduced. The enhanced model can meet the requirement of accurate real-time recognition of license plates under strong snowfall weather conditions.

The Image Classification Algorithm Was Implemented on The MNIST Data Set

In the current era of rapid development of science and technology, the recognition and classification of digital images is the key to solving many problems, such as the application of license plate recognition, document digitization, and remote sensing image surface classification. Based on the MNIST handwritten numerical data set collected by the National Institute of Standards and Technology (NIST), this report uses Python language and PyTorch programming framework to construct a convolutional neural network (CNN) structure and practice and experience the image classification of handwritten digits in the MNIST data set.

Design of Chinese License Plate Recognition and Parking Billing System based on AI

Design of Chinese License Plate Recognition and Parking Billing System based on AI

A Survey on Comprehensive Exploration of Modern Perception System

This review paper offers an in-depth analysis of recent advancements in image-based intelligent transportation systems. It provides insights into various approaches employed for detecting false images, license plate recognition, distributing emergency messages, as well as detecting and classifying vehicle accidents and road accidents. The research critically evaluates the pros and cons of deep learning techniques, including position-based routing, enhanced YOLOv8, optimal K-means with a convolutional neural network, and gated hierarchical multi-task learning. The investigation of an Internet of Things-based vehicle accident detection and classification system highlights the benefits of sensor fusion for precise accident detection; nonetheless, the evaluation raises questions regarding the scalability and reliability of smartphone sensors. The utility of a deep learning system for road accident identification utilizing artificially created multi-perspective movies is emphasized, along with warnings about possible limits in capturing real-world circumstances

가상박물관 플랫폼을 활용한 DMZ의 문화적 확장성 방안 연구

The purpose of this study is to use a virtual museum platform to expand the cultural and artistic experiences related to the DMZ (Demilitarized Zone). The DMZ, with its historical, political, military, ecological, and geographical significance, has gained global attention, vividly illustrating the unique geopolitical characteristics of the Korean Peninsula involving peace, confrontation, and coexistence. It is noteworthy that existing cultural and artistic activities in the DMZ often rely on direct participation. However, due to the challenges of accessibility, limited information, and cultural differences over time associated with the DMZ, there are difficulties in expanding cultural and artistic content.Therefore, in this study, we assume that digital platforms such as virtual museums utilizing 3D laser projection mapping or 3D scanning will play a crucial role in addressing these limitations. We aim to analyze the current situation and potential solutions by examining existing cultural and artistic activities within the DMZ, considering the role of these digital platforms in overcoming the identified challenges.

E-Challan Based Intelligent Assistance System for Smart City

Traffic management and enforcement of road safety regulations have become increasingly crucial in modern urban environments, as the number of vehicles on the roads continues to surge. With the rise in traffic violations, particularly signal disobedience, there is a pressing need for innovative solutions that can efficiently detect and penalize offenders while reducing the burden on law enforcement personnel. The E-Challan System harnesses the power of cutting-edge computer vision techniques, most notably Automatic Number Plate Recognition (ANPR), to accurately capture and decipher vehicle number plates in real- time. High-resolution cameras strategically positioned at traffic junctions and signal intersections actively monitor passing vehicles, automatically detecting those that violate traffic signals. Upon successful identification of a signal violation, the E-Challan System promptly generates an electronic challan, or e-challan, containing detailed information about the violation, such as the date, time, and location of the incident. This comprehensive documentation serves as indisputable evidence of the offense and provides an essential legal foundation for imposing fines on violators. with an Auto Debit Fine mechanism.

Penerapan Metode Interpolasi Linear dan Geometric Mean Filter Pada Citra Resolusi Rendah Hasil Resampling

In addition to the color representation contained in a digital image, the size of the image will also greatly affect the image processing process. In some cases of image acquisition, sometimes the size of the image obtained does not correspond to the desired one. Pattern recognition processes, such as vehicle license plate recognition, may result in incorrect output if the size of the processed input image is not appropriate. Therefore, determining the size of a digital image is very important in further image processing. To solve such problems can be done by enlarging the size of the image through the process of interpolation or resampling. Image magnification is the process of enlarging the size of a digital image from its original size to a new and different size based on the desired magnification scale. However, as a result of enlarging the image obtained there is always a blur, so the image will look like a checkerboard (split). This situation occurs because during the magnification process the resolution of the new image becomes lower. Thus, additional methods are needed to improve the image quality of the resampling results. This study aims to improve the low-resolution image quality of resampling results by applying Linear Interpolation and Geometric Mean Filter methods. The test results showed that the Linear Interpolation method was able to produce a new image that was larger than the original image and the Geometric Mean Filter method could improve the image quality of the enlarged image so that the details of the information in the image are better and clearly seen visually. And to find out if the image enlarged from the Linear Interpolation and Geometric Mean Filter is good or not, we will use the Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) methods. Keywords : Image, Interpolasi Linear,Geometric Mean Filter, PSNR, MSE.

Ethical Implications of Advanced Surveillance Technologies on Law Enforcement: A Case Study of National Police Service in County of Nairobi, Kenya

The study's research objectives included examining the effects of advanced surveillance technologies on law enforcement by NPS in Nairobi County and evaluating the ethical implications of using these technologies by the NPS. The social contract theory was used to understand the ethical implications of using advanced surveillance technologies in law enforcement. The research population includes the 4.7 residents of Nairobi County affected by the implementation of advanced surveillance technologies both overtly and covertly. The data was collected from a sample of 50 randomly sampled participants. The respondents were engaged using a qualitative questionnaire. Therefore, both qualitative and quantitative data were collected in the study. The study was analyzed using Statistical Package for Social Science (SPSS) and the thematic analysis method. The study concluded that the use of advanced surveillance technologies such as CCTV systems, facial recognition, license plate recognition, drones, forensic tools, and biometric systems in law enforcement and public safety are widely acknowledged. The ethical considerations regarding these technologies include data security, privacy, freedom of expression, transparency, and accountability. These, however, do not negate the fact that the technologies are necessary for enhancing public safety and emergency response. It is, therefore, imperative to establish clear guidelines and regulations for using advanced surveillance technologies in law enforcement

Automatic License Plate Recognition with CNN Method in Machine Learning

In this period of consistently expanding innovation, there is a tremendous interest among individuals in a no-problem-at-all day-to-day way of life and travel. With the gigantic improvement in the vehicular area consistently, following individual cars has turned into an undeniably challenging task. With reconnaissance cameras on the side of the road, this thought proposes automatic license plate recognition for vehicles rushing. To tackle this issue, a proficient deep learning model called a Convolutional neural network is utilized for object identification and Simple OCR for character recognition. ALPR (Automatic License Plate Recognition), quite possibly one of the most broadly utilized computer vision applications is the subject of the proposed work. Furthermore, the paper provides insights into publicly accessible evaluation metrics and benchmark results, establishing a standardized framework for the quantitative assessment of automatic license plate recognition research employing CNN image resnet. The ultimate goal is to contribute to a deeper understanding of the latest state-of-the-art studies in automatic license plate recognition, particularly within the context of CNN image resnet in machine learning.

Advantages of Automated License Plate Recognition Technology

Automatic Number Plate Recognition (ANPR) is a technology that can be utilized by mall and movie theater parking management as well as toll booths on roads, expressways, etc. to expedite the toll collection process. Automated systems to retain vehicle information are becoming more and more important as the number of vehicles rises. It is imperative that communication be prioritized in order to effectively manage traffic and reduce crime. Number plate recognition is a dependable technique for automatic vehicle identification. With current algorithms, which are based on the concept of learning, developing adequate outcomes takes a long time and a lot of experience. An innovative use of machine learning is Automatic Number Plate Detection, which recognizes photos and transforms them into text. Using picture segmentation, the system locates, picks up, and extracts the vehicle's image and number plate. Afterwards, optical character recognition technology is used to identify characters in the retrieved image. This technology is used in military zones, apartment buildings, and traffic surveillance.

Effect of rough screen on speckle suppression by wavelength and angle diversity in laser projection systems

In speckle suppression research, screens play a critical role in formation of speckle. This paper examines screen speckle using diverse light sources and screens of varying roughness. Our findings demonstrate that speckle suppression by screens is the key reason wavelength and angle diversities are not mutually independent in existing literature. Different from the theory, our experiments reveal that increased screen roughness can effectively suppress speckle for narrow-spectrum blending light source. This method also enhances the effectiveness of speckle suppression elements. These results contribute to refining current speckle evaluation models and aid in designing screens that yield lower speckle contrast.

A License Plate Detection and Recognition Method Based on Improved Yolov5

This study proposes a license plate detection and recognition method based on yolov5. In order to accurately detect the license plate and accurately identify the license plate number, this study adds key point detection based on the yolov5 algorithm to correct the license plate image into a rectangle through perspective transformation, which greatly improves the accuracy of license plate number recognition. Due to embedded deployment, this study simplifies the yolov5 network structure to a certain extent. While reducing the network depth and width, ShuffleNet Block is used to replace the C3 module in yolov5. In the end, this study achieved a mean average precision (mAP) of 89.7% while controlling the network calculation amount to 5GFLOPs. Therefore, this model has broad application prospects in the transportation field.

A Chinese License Plate Recognition System based on OpenCV for complex environments

License plate recognition technology is a key to realizing intelligent traffic management. Nowadays, most of the license plate recognition technologies are only designed for the preferred lighting conditions. But in the natural scenes, the change of the weather and the light intensity throughout the day carry many difficulties to the location and recognition of the license plate system. To solve this problem, we proposed a license plate recognition system based on OpenCV and SVM (Support Vector Machines) in this paper. First, the Sobel Edge Detection algorithm combined with Mathematical Morphology was used to determine the approximate outline of the license plate. And Orthographic Projection was then used to segment the characters in the license plate. Finally, the segmented characters were identified by using SVM. The recognition accuracy of the proposed system is 96.52%. In order to improve the operability of the system, we have designed a Graphical User Interface (GUI) based on PyQt5, which makes the interface more beautiful and simple.

Assessing the Accuracy of Average Travel Speeds from Floating Car Data on Urban Street Facilities

To improve traffic flow on regularly congested roads in a network, the performance of these roads has to be evaluated. The average travel speed is a crucial indicator of traffic quality and is used in several performance evaluation methods. Floating Car Data (FCD) obtained from vehicles equipped with GPS offers continuous and ubiquitous data acquisition, making it a potential alternative to conventional speed detection methods. However, the limited penetration and representativeness of detected vehicles pose challenges. This work investigates the suitability of commercially available FCD for determining accurate space-mean speeds. Ground truth data was collected using Automatic Number Plate Recognition (ANPR), and a comparison analysis was performed with FCD-derived travel times and space-mean speeds. Different statistical measures were applied to evaluate the accuracy of FCD data. The results indicate a generally acceptable correlation between the two data sources, but deviations occur, especially for time intervals with a low number of detected FCD trajectories. Subsequent analyses suggest that a minimum number of five trajectories per hour are sufficient to derive valid space-mean speeds. Based on these findings, it is possible to accurately assess the performance of roads exclusively using commercially available FCD.

Low Light Image Enhancement in License Plate Recognition using URetinex-Net and TRBA

The license plate recognition system currently in use is susceptible to interference from the external environment and performs poorly in low-light conditions. This paper presents a solution for license plate recognition under a low-light environment. We adopted URetinex-Net methods that unfold an optimization issue into a learnable network to decompose a low illumination image into reflectance and illumination layers. We also adopted TRBA, an end-to-end recognition method involving no character segmentation. The experimental results show that the accuracy of the night environment of the proposed method is 80.11% increased by 5.11% compared to without the low light image enhancement method.

Traffic Management through Cutting-Edge Vehicle Detection, Recognition, and Tracking Innovations

In this research, we explore the latest advancements in the domains of vehicle detection, recognition, and tracking through vision-based methodologies. Spanning from traditional techniques to cutting-edge deep learning models, the paper provides a thorough analysis of their strengths, limitations, and real-world applications. We examine the evolution of detection methods, including edge-based approaches and motion segmentation techniques like frame differencing, background subtraction, and optical flow, showcasing their efficacy in identifying moving vehicles. Convolutional neural networks (CNNs), in particular, are highlighted for the integration of appearance-based approaches due to their transformative impact on accuracy, particularly in difficult settings. In addition, we explore vehicle recognition and classification methods, highlighting their applications in traffic analysis, security, and urban planning. These methods include color recognition, license plate recognition, logo recognition, and vehicle type classification. In addition to comparing tracking algorithms based on accuracy, computing economy, and adaptability, the evaluation includes methodologies for tracking vehicles that include model-based, shape-based, and feature-based approaches. In conclusion, this research provides a thorough examination of the development of vision-based vehicle analysis, making it a useful tool for academics, professionals, and decision-makers working to improve traffic surveillance systems and plan for the future of urban transportation.

Automatic Identification and Tracking Method of Case-Related Vehicles Based on Computer Vision Algorithm

Abstract Amidst the rapid advancement of artificial intelligence, computer vision algorithms have found extensive applications across various societal sectors. This paper presents the development of an automatic vehicle identification algorithm for crime-related scenarios, leveraging enhancements in computer vision technology. Initially, we refine the multi-scale feature fusion within the YOLOv4 architecture, subsequently substituting the standard convolution in the feature extraction network with depth-separable convolution to minimize parameter computation. Furthermore, we replace the conventional CIOU target localization loss function with EIOU to expedite model convergence. To address the issue of target vehicle detection failures, we incorporate the Kalman filter algorithm, ensuring precise tracking. Our experimental analysis, which utilizes both target detection and multi-target tracking evaluation indices, demonstrates that the modified YOLOv4 algorithm excels in recall, precision, and average IOU metrics compared to other algorithms, with a mean Average Precision (mAP) of 95.68% and an average detection speed of 0.039 seconds per image, satisfying real-time operational criteria. Vehicle tracking efficacy, compared using the YOLOv4-based detector before and after the modifications, shows significant improvements, indicated by a reduction in Identification Switches (IDS) across all video samples. This study introduces a robust method for the accurate identification and localization of vehicles involved in criminal activities, significantly enhancing case resolution efficiency.