License Plate Recognition Research Articles

Docker Container-Based Framework of Apache Kafka Node Ecosystem: Vehicle Tracking System by License Plate Recognition on Surveillance Camera Feeds

Vehicle tracking and license plate recognition (LPR) over video surveillance cameras are essential intelligent traffic monitoring systems. Due to the enormous amount of data collected each day, it would be difficult to track vehicles by license plate in a real-world traffic setting. Large volumes of data processing, real-time request responses, and emergency scenario response may not be possible using conventional approaches. By combining license plate recognition with the docker container-based structure of the Apache Kafka node ecosystem, the suggested solution takes a novel approach to vehicle tracking. The primary components of our suggested framework for reading license plates are the identification of license plates and text data queries. License plate localization is performed with You Only Look Once version 3 (YOLOv3) and character recognition with Optical Character Recognition (OCR). The detected vehicle images with license plate results are published on related topics with Apache Kafka. Apache Kafka is a publish-subscribe (producers-consumers) messaging system and one of the most popular architectures used for streaming data. For each license plate search, a topic will be created in the framework where producers publish and consumers receive data. Thus, the workload of the operators will be reduced and they will be able to pay attention to more important events in traffic.

Vehicle Detection in Traffic Monitoring

Traffic Monitoring is a challenging task on crowded roads. Traffic Monitoring procedures are manual, expensive, time consuming and involve human operators. Large-scale storage and analysis of video streams were not possible due to limited availability. However, it is now possible to implement object detection and tracking, behavioral analysis of traffic patterns, number plate recognition and surveillance on video streams produced by traffic monitoring. In Big data, video datasets are so large that typical database systems are not able to store and analyses the datasets. Storing and Processing big volumes of data requires Scalability, Fault Tolerance and Availability. Thus, Big Data and Cloud computing are two compatible concepts as cloud enables big data for traffic monitoring. In this paper, we proposed vehicle detection for traffic monitoring. We have applied Support Vector Machine (SVM) machine learning algorithm for detecting vehicles.

Smart Parking System for Navi-Mumbai Airport

A commercial airport parking system has to deal with many problems such as unnecessary traffic congestion which is mainly caused due to the lack of smart and systematic parking. This eventually causes the time loss of the users while causing unwanted delays in their individual journey. Due to the lack of employees as well as inefficiency in their employment skills, the users again have to face various problems. All these problems ultimately lead to dissatisfaction in the experience of users. Its final capacity will be to handle 90 million passengers. Considering the problems mentioned above, we are providing a designed parking layout which includes a ground floor plan and a typical floor plan with all the basic amenities like washrooms, staircases, waiting areas, elevators, etc. This paper analyses a few parking apps and software and then suggests to build a dedicated app for the airport parking system which will help to counter the problems faced while enhancing the user’s experience. The smart features which should be included in the app are license plate recognition, online parking prepayment, parking guidance, vehicle Detection sensors and automated payment stations, unique code for the parking slot of the user's vehicle. Keywords – applications, airports, mlcp, Smart Parking systems, technologies

Detection of Vehicle Number Plate and Speed Using Machine Learning

In an era marked by a significant upsurge in vehicular traffic and the resultant increase in road accidents, the need for advanced traffic management systems is paramount. This paper presents an innovative solution, "Detection of vehicle number plate and speed using machine learning" which aims to enhance road safety by addressing the persistent problem of over-speeding. The project leverages state-of-the-art technologies, including machine learning, deep learning, and computer vision, to develop a smart system that can detect speeding vehicles, recognize license plates, and record crucial data related to speed limit violations. The primary motivation for this endeavor is the escalating rate of road accidents, particularly in India, driven by speeding in areas susceptible to accidents. The system employs image segmentation, corner detection algorithms, filtering algorithms, and automatic vehicle plate recognition techniques to achieve its objectives. By using machine learning and deep learning models, the system accurately identifies license plates, segments characters on these plates, and recognizes them. In summary, this project represents a significant advancement in road safety and traffic management. By deploying an intelligent system that can proactively detect over-speeding vehicles and maintain comprehensive records, it strives to create a safer and more responsible driving environment, ultimately working towards the prevention of accidents and saving lives. Key Words: Automatic Number Plate Recognition, Vehicle Speed Detection, Machine Learning, Computer Vision, Traffic Management

Smart Traffic Surveillance: Identifying Helmet-Less Riders and License Plate Recognition with YOLO and OCR Integration using Deep Learning

Currently, there are a number of issues with Indian traffic laws that can be resolved in a variety of ways. Riding a motorbike without a helmet is against the law and has increased the number of accidents in India. The current method mostly uses CCTV records to monitor traffic offences. When a rider is not wearing a helmet, traffic officers must zoom in on the licence plate and look into the frame where the infraction is occurring. However, because there are more and more traffic infractions and motorcycle ridership every day, this will take a lot of labour and time. What if there was a technology that would automatically search for traffic violations such as riding a motorbike without a helmet? hence, would automatically retrieve the number plate number of the bike. This study has been effectively completed by recent research using KNN, DNN-Classifier, etc. However, there are limitations to these works in terms of speed, accuracy, or efficiency in object identification and classification. The goal of this research project is to develop a non- helmet rider detection system that can automatically identify and retrieve a vehicle's number plate number when a driver fails to wear a helmet. Three-level Deep Learning for Object Detection is the key idea at play here. The items found are a human, a motorcycle at level one using YOLO, a helmet at level two using YOLO, Licence plate at the last level using YOLO. Next, OCR is used to obtain the number plate registration number. All of these methods—especially the section where the number plate number is extracted—are subject to predetermined limitations and conditions. Because video is the work's input, speed of execution is essential. We have developed a comprehensive algorithm for both number plate number extraction and helmet detection using the above-mentioned approaches.

Development of a vision system integrated with industrial robots for online weld seam tracking

This paper develops a laser-structured light vision system for the industrial welding manipulator. The designed structured light vision system consisting of a line laser projector and an industrial camera is a decent 3D object data acquisition platform. By applying the principle of triangulation, the depth value of the object can be obtained via the laser plane in the captured image. According to hand-eye calibration and the principle of triangulation, the 3D position of the objects in the robot coordinate system is calculated. Based on the discriminated convolution tracker and deep reinforcement learning that increases the accuracy and processing time of the tracker, allowing simultaneous scanning for seam data acquisition and welding along the weld path, a real-time seam tracking algorithm is developed to handle the square-groove butt joint with small gaps (i.e., <1 mm). Experiments are undertaken using a Yaskawa industrial welding robot integrating the designed laser-structured light vision system. Experiment results show the significant performance of the developed system in welding square-groove butt joints with curved profiles and small gaps, i.e., the welding absolute mean error is 0.31 mm, whereas in the case of using convolution tracker only, the welding absolute mean error 1.7 mm.

Two-stage HOG/SVM for license plate detection and recognition

Automatic license plate recognition (ALPR) is one of the technologies used in intelligent transport systems (ITS) to read vehicle license plates automatically. The extracted information has various potential applications, including but not limited to an electronic payment gateway, a system for paying parking fees, road surveillance, and managing traffic flow. In this paper, we propose an efficient method to detect and identify the Algerian license plate (LP). This method consists of a two-stage algorithm that combines the histogram of oriented gradients (HOG) with the support vector machine (SVM) classifier. The purpose of the first stage of HOG/SVM is the detection of the LP, while the recognition of the digits is accomplished by the second stage of HOG/SVM. As first contribution, a dataset of standard Algerian LP not available elsewhere is built (DZLP dataset), The second is a proposal of a very efficient pre-processing step for LP detection and digit recognition. Experimental results show that the proposed approach yields very high license plate and average digits recognition rates, which of 97.5% and 99.46%, respectively.

NUMBER PLATE DETECTION USING MACHINE LEARNING

This project seeks to develop a real-time, Python-based, license plate recognition system leveraging deep learning's efficiency and robustness. Employing custom- trained convolutional neural networks alongside targeted object detection algorithms, the system aims to accurately locate and extract license plate information from images and video streams. Extensive data preprocessing and comprehensive model training optimize performance under varying lighting, angles, and minor occlusions. Integration into a user-friendly application further enhances accessibility and practical utility. This project's contribution lies in its applicability to real-world scenarios like traffic management, law enforcement, and vehicle tracking, demonstrating the power of advanced computer vision and machine learning for practical problem-solving in the automotive domain.

4‐1: Invited Paper: Effective Speckle Reduction in Laser Projection Systems Using a Low Cost Screen with Multilayer Structure

4‐1: <i>Invited Paper:</i> Effective Speckle Reduction in Laser Projection Systems Using a Low Cost Screen with Multilayer Structure

AutoID GateKnight: Empowering Gate Security with License Plate Recognition and Vehicle Colour Detection

Abstract: AutoID gateKnight represents a significant breakthrough in gate security technology. Its utilization of License Plate Recognition and vehicle color detection technologies not only strengthens access control but also offers a level of adaptability and customization that is unparalleled. At the entrance gate, the system will be able to identify licence plates from passing cars and determine whether or not to allow the vehicle access. The gate will operate automatically, with no human interaction. The device is made up of a video camera that records video frames, which contain a picture of the licence plate and the colour of the car. In order to build the suggested system, colour detection using a colour histogram, easyocr to extract the number plate from the image, and Haar cascade for number plate detection have all been used. We process the number plate and colour of the car after properly identifying it, and then we compare it to the vehicle's authorization and registration status. By compiling both the number plate and color of the vehicle, this system provides a robust and highly accurate result. Additionally, the system maintains an updated database of the cars that have left and entered into the required places within a particular duration.

An Optimized Dynamic Traffic Control System using OpenCV

Abstract: This study delves into a web-based platform designed to apprehend speeding vehicles. It harnesses the capabilities of OpenCV, a robust computer vision library, to accurately detect high-speed cars. Furthermore, the system integrates a specialized model for identifying vehicles deviating from their designated lanes, thereby fostering safer road conditions. To streamline the process of vehicle identification and potential ticket issuance, it incorporates automatic license plate recognition through Azure Cognitive Services. By amalgamating these advanced technologies, the proposed system offers a comprehensive solution for realtime traffic monitoring and regulation enforcement, with the overarching goal of enhancing road safety and traffic management.

Smart and Secure Vehicle Parking System to Avoid Theft Using Deep Image Recognition

Secure intelligent parking systems are complicated issues around the world. A secure parking system that is larceny-free is considered a luxury now. Parking is expensive and limited in almost all large cities. This research study presents a unique, safe, intelligent parking system based on deep image recognition techniques. To solve this tedious problem, an object detection deep learning model such as You Only Look Once (YOLO) is used. The proposed model is divided into three steps. The first step captures the car's image while entering the parking area. The second step is License plate recognition, in which the license plate is detected and saved the individual characters, and the recognized dataset is used when the driver leaves the parking area. The third step is facing detection and recognition of the taken plate. The fourth step is that the printer generates an invoice code used in case of a mismatch in the face of the driver. Evaluation of the proposed model indicates its efficiency. Accuracy of the following modules is achieved for vehicle detection (99%), number plate detection and recognition 98%, and 95%, respectively, and face detection and recognition 99%, and 97%, respectively. The proposed model's efficiency is shown through its evaluation. To get more security, a two-way screening procedure is used to keep the vehicles secure. So, the identification system's unique idea is to achieve better results by using two cameras and two-way authentication. It also forbids the entrance of those vehicles and drivers who are unauthorized or not allowed by the firm. Moreover, these modules were successfully implemented and evaluated using various performance measures, including precision, recall, and mean average precision.

Automated License Plate Recognition for Non-Helmeted Motor Riders Using YOLO and OCR

One of the leading causes of serious injuries in motorcycle accidents is the failure to wear a helmet, pressing the need for effective measures to encourage riders to use helmets. To stop these frequent violations of business regulations, regular observation is necessary. The proposed system is for detecting traffic violations in India related to riding motorcycles without helmets. The system utilizes deep learning-based object detection using YOLO and OCR techniques to automatically detect non-helmet riders and extract license plate numbers. The proposed system aims to improve efficiency and accuracy in detecting violations by automating the process and reducing the need for manpower. The system involves three levels of object detection: person and motorcycle/moped, helmet, and license plate. OCR is used to extract the license plate number, and all techniques are subject to predefined conditions and constraints. The system is designed to operate on video input to ensure high-speed execution, and it intends to offer a complete solution for both detection of helmet and extracting the license plate number.

Unleashing the power of Convolutional Neural Networks in license plate recognition and beyond

This article explores the application of Convolutional Neural Networks (CNN) in the field of license plate recognition. It begins by introducing the architecture of CNN, which consists of three key layers: Convolutional Layers, Pooling Layers, and Fully Connected Layers. The article then references three relevant papers that demonstrate how CNNs are applied in license plate recognition. The first paper utilizes TensorFlow to construct a CNN model and integrates it with an STM32MP157 embedded chip for license plate recognition. The second paper presents a real-time car license plate detection and recognition method called Multi-Task Light CNN, emphasizing robustness. The third paper employs the ResNet+FPN feature extraction network of the Mask R-CNN model and annotates a license plate dataset. The article highlights the promising future of CNNs in various fields beyond license plate recognition, emphasizing their potential for further development and industrial applications. CNNs have proven to be versatile and powerful tools in computer vision, offering solutions to a wide range of problems. Their adaptability and effectiveness make them a key player in the ongoing advancement of artificial intelligence and automation technologies.

Study On Advanced Vehicle Number Plate Detection Systems-A Review

In response to the rising volume of vehicles on roadways, there's an urgent demand for advanced technologies to bolster security and streamline traffic-related operations. This project introduces an innovative solution: an Advanced Automatic Number Plate Detection System. Leveraging Python, Easy OCR, and Open CV, this system aims to develop a sophisticated Automatic Number Plate Recognition (ANPR) mechanism capable of accurately detecting license plates, extracting alphanumeric characters, and providing actionable insights for various applications. Python is chosen for its versatility and ease of use, serving as the primary programming language. OpenCV is employed for its robust image processing tools and license plate detection capabilities, while Easy OCR simplifies character extraction from license plates. Key features include license plate detection through OpenCV algorithms, character recognition via Easy OCR, and real-time processing suitable for tasks like traffic monitoring and surveillance. The project follows a structured approach encompassing data collection, image pre-processing, machine learning model training, integration, and thorough testing. Anticipated outcomes include a fully operational ANPR system with high accuracy in license plate detection and character recognition, surpassing conventional methods. Its potential integration into real-world applications such as traffic management underscores its significance in addressing challenges posed by increasing vehicle numbers, with YOLO v8 utilized for precise outcomes. Keywords- Python, Open CV, YOLO, tensorflow, Machine learning, ANN, CNN, and Easy OCR

Automated license plate authentication framework using multi-view vehicle images

The current framework for detecting Fake License Plates (FLP) in real-time is not robust enough for patrol teams. The objective of this paper is to develop a robust license plate authentication framework, based on the Vehicle Make and Model Recognition (VMMR) and the License Plate Recognition (LPR) algorithms that is implementable at the edge devices. The contributions of this paper are (i) Development of license plate database for 547 Indian cars, (ii) Development of an image dataset with 3173 images of 547 Indian cars in 8 classes, (iii) Development of an ensemble model to recognize vehicle make and model from frontal, rear, and side images, and (iv) Development of a framework to authenticate the license plates with frontal, rear, and side images. The proposed ensemble model is compared with the state-of-the-art networks from the literature. Among the implemented networks for VMMR, the Ensembling model with a size of 303.2 MB achieves the best accuracy of 89% . Due to the limited memory size, Easy OCR is chosen to recognize license plate. The total size of the authentication framework is 308 MB. The performance of the proposed framework is compared with the literature. According to the results, the proposed framework enhances FLP recognition due to the recognition of vehicles from side images. The dataset is made public at https://www.kaggle.com/ganeshmailecture/datasets.

Study on Speckle Noise Reduction in Laser Projection Displays

Laser speckle has a negative effect on laser projectors, so reducing laser speckle is crucial for the development of laser projector displays. We primarily focus on studying the laser speckle contrast of laser projector displays and the mechanism for reducing speckle. Based on the theory of decreasing temporal and spatial coherence of laser light, this report derives the complete formula for calculating speckle contrast in a laser projector display and provides detailed calculation procedures. According to the comprehensive formula, the primary factors influencing speckle contrast encompass wavelength, spectrum, angles of incidence or observation of lasers, the roughness of the screen surface, the number of independent speckle patterns generated by a moving diffuser, and the number of resolution elements within one eye resolution element in the projector lens. Various methods have been used in the projection engine to suppress speckle, and the main factors for reducing speckle have been verified through theoretical calculations and experimental verification. At a testing distance of 700 mm and with an F-number of 41.7 for the detector lens, the RGB laser speckle contrasts were measured to be 9.1%, 7.3%, and 10.4%, respectively, which aligns well with the results obtained from theoretical calculations. Meanwhile, the speckle contrast of the white field was also measured, yielding a result of 5.6%. The speckle contrast becomes imperceptible when the viewing distance exceeds 2000 mm in our projection system.

Advancements in Computer Vision Applications for Traffic Surveillance Systems

With the increasing need for efficient traffic surveillance systems, computer vision emerges as a promising technology to address various challenges in this domain. This paper aims to provide an overview of recent advancements in the application of computer vision in traffic surveillance. It commences by discussing fundamental concepts of computer vision and its role in traffic monitoring, followed by an exploration of various techniques and algorithms used in computer vision systems, including object detection, tracking, and classification. Furthermore, it investigates the integration of computer vision with other technologies such as machine learning and deep learning, emphasizing their impact on enhancing the accuracy and efficiency of traffic surveillance systems. The review explores specific applications of computer vision in traffic surveillance, such as traffic flow monitoring, anomaly detection, and license plate recognition. Additionally, it addresses challenges and limitations associated with the implementation of computer vision in real-world traffic scenarios. Synthesizing existing literature, case studies, and research findings, this review offers a comprehensive insight into the current state-of-the-art in the application of computer vision for traffic surveillance systems. The paper concludes with a discussion of future directions and potential research opportunities in this rapidly evolving field.

Helmet Detection and Number Plate Recognition Using YOLOv8 and Tensorflow Algorithm in Machine Learning

In many suburbs, urban areas and locals across the globe there is a disconcerting surge in motorcycle accidents has become increasingly evident. Even though a helmet is the most important safety gadget, people do not wear it. Yet helmets stand as indispensable guardians shielding riders from the ravages of head injuries, an all-too-common occurrence in the chaotic landscape of accidents. People even knowing the importance of helmet ignore wearing them, which prone to accidents causing head injuries and elevate the collective standard of road safety therefore it is incumbent upon both the governing bodies and individual riders alike to vigorously champion and enforce the unwavering and meticulous utilization of helmets as an inseparable facet of conscientious motorcycle navigation.

PURP: A Scalable System for Predicting Short-Term Urban Traffic Flow Based on License Plate Recognition Data

PURP: A Scalable System for Predicting Short-Term Urban Traffic Flow Based on License Plate Recognition Data