Vehicle Identification Research Articles

Implementation of Internal Organizational Communication in the Motor Vehicle Registration and Identification (Regident) System in the South Sulawesi Police Traffic Directorate

The purpose of this study is to analyze the implementation of organizational communication from the aspect of internal communication in the Motor Vehicle Registration and Identification (Regident) System at the South Sulawesi Regional Police Traffic Directorate. The research uses a quantitative and qualitative approach with an Explanatory Design research design. The results show that internal organizational communication plays a very important role in the context of implementing information technology for the Motor Vehicle Registration and Identification (Regident) System services at the South Sulawesi Regional Police Traffic Directorate. Here are some key points to consider: first, Coordination and Collaboration where internal organizational communication facilitates effective collaboration between various units or departments within the Traffic Directorate. Second is Information Dissemination where effective communication ensures that all staff understand the latest changes or developments in the Regident system, including the implementation of new technology or improvements made. Third is Motivation and Engagement where good communication can increase staff motivation and their engagement in achieving organizational goals, and fourth is Conflict Resolution where open and clear communication helps in addressing conflicts or disagreements among staff or departments that may arise in the management of Regident.

Evaluation of Run-off-Road Crashes Involving Battery-Electric Vehicles

Electric vehicles (EVs) are increasing in popularity and are among the fastest-growing segments in new vehicle sales. Battery-electric vehicles (BEVs) are the highest-selling class of EV. BEVs use a unique frame construction (i.e., a “skateboard” chassis) with a heavy battery pack located below the occupant’s floorpan, and are heavier and have lower centers-of-gravity than internal combustion engine vehicles (ICEVs)—factors that could affect their crashworthiness with roadside features. A crash study was performed to compare the crash conditions for BEVs to ICEVs and hybrid-electric vehicles (HEVs) covering the numbers of vehicles, posted speed limits, crash locations, time of day, weather conditions, crash severities, and roadside fixed objects struck. Eight state departments of transportation provided crash data spanning 5 years that included vehicle identification numbers or make, model, and year combinations, which were used to isolate BEVs from ICEVs. Crash results were reviewed and EV crash conditions were found to be similar to those of ICEV and HEV crash conditions. Moreover, the frequency of run-off-road crashes was similar for BEVs and ICEVs. Because no substantial differences were observed between the two datasets, it is recommended that impact conditions described in the Manual for Assessing Safety Hardware be utilized during preliminary evaluation of roadside hardware using electric vehicles.

Enhancing vehicle detection in intelligent transportation systems via autonomous UAV platform and YOLOv8 integration

This study highlights the evolving landscape of object detection methodologies, emphasizing the superiority of deep learning-based approaches over traditional methods. Particularly in intelligent transportation systems-related applications requiring robust image processing techniques, such as vehicle identification, localization, tracking, and counting within traffic scenarios, deep learning has gained substantial traction. The YOLO algorithm, in its various iterations, has emerged as a popular choice for such tasks, with YOLOv5 garnering significant attention. However, a more recent iteration, YOLOv8, was introduced in early 2023, ushering in a new phase of exploration and potential innovation in the field of object detection. Consequently, due to its recent emergence, the number of studies on YOLOv8 is extremely limited, and an application in the field of Intelligent Transportation Systems (ITS) has not yet found its place in the existing literature. In light of this gap, this study makes a noteworthy contribution by delving into vehicle detection using the YOLOv8 algorithm. Specifically, the focus is on targeting aerial images acquired through a modified autonomous UAV, representing a unique avenue for the application of this cutting-edge algorithm in a practical context. The dataset employed for training and testing the algorithm was curated from a diverse collection of traffic images captured during UAV missions. In a strategic effort to enhance the variability of vehicle images, the study systematically manipulated flight patterns, altitudes, orientations, and camera angles through a custom-designed and programmed drone. This deliberate approach aimed to bolster the algorithm's adaptability across a wide spectrum of scenarios, ultimately enhancing its generalization capabilities. To evaluate the performance of the algorithm, a comprehensive comparative analysis was conducted, focusing on the YOLOv8n and YOLOv8x submodels within the YOLOv8 series. These submodels were subjected to rigorous testing across diverse lighting and environmental conditions using the dataset. Through tests, it was observed that YOLOv8n achieved an average precision of 0.83 and a recall of 0.79, whereas YOLOv8x attained an average precision of 0.96 and a recall of 0.89. Furthermore, YOLOv8x also outperformed YOLOv8n in terms of F1 score and mAP, achieving values of 0.87 and 0.83 respectively, compared to YOLOv8n's 0.81 and 0.79. These outcomes of the evaluation illuminated the relative strengths and weaknesses of YOLOv8n and YOLOv8x, leading to the conclusion that YOLOv8n is well-suited for real-time ITS applications, while YOLOv8x exhibits superior detection capabilities.

Pemodelan Identifikasi Objek Kendaraan Bermotor Menggunakan Faster Region based Convolutional Neural Network (R-CNN) Berbasis Python

The vehicles are currently experiencing a surge in number and variation. This is evident from the kinds of vehicles that are passing through the highway area. The rise in the number of motorized vehicles will surely give a squeeze to the traffic density. The increase in the number of motor vehicles is one of the biggest factors in the impact of the congestion. The congestion can also cause damage to the highway. It's supposed to be the focus of the local government in dealing with the problem. Each road point has its own potential, so it is necessary to have a calculation in identifying the number of vehicles and the type of vehicles that are slipped on the road. Motor vehicle identification can be solved using the Faster Region based Convolutional Neural Network approach. Faster R-CNN is a deep learning architecture used to detect inside computers. Research will run at several highway points to take samples of video at a certain time, for identified the type of vehicle. Vehicle labelling will facilitate the calculation of the number of vehicles crossing the road in a given unit of time. The vehicle identification needs are used to see the density of the highway so that it can help the local government in making the right decision or solution to reduce the traffic density. The results of research such as quantitative data can be easily used to give the right picture and decision.

Method of Identification of Combat Vehicles Based on Yolo

A method for recognizing contours of objects in a video data stream is proposed. Data will be uploaded using a video camera in real time and object recognition will be performed. We will use the YOLO network – a method of identifying and recognizing objects in real time. Recognized objects will be recorded in a video sequence showing the contours of the objects. The approach proposed in the project reasonably synthesizes methods of artificial intelligence, theories of computer vision on the one hand, and pattern recognition on the other; it makes it possible to obtain control influences and mathematical functions for decision-making at every moment of time with the possibility of analyzing the influence of external factors and forecasting the flow of processes, and refers to the fundamental problems of mathematical modeling of real processes. The installation of the neural network is shown in detail. The characteristics of the neural network are shown and its capabilities are substantiated. Approaches to computer vision for object extraction are shown. Well-known methods are methods of expanding areas, methods based on clustering, contour selection, and methods using a histogram. The work envisages building a system for rapid identification of combat vehicles based on the latest image filtering methods developed using deep learning methods. The time spent on identifying the machine will be 10 –20 % shorter, thanks to the developed new information technology for detecting objects in conditions of rapidly changing information.

A non‐contact identification method of overweight vehicles based on computer vision and deep learning

AbstractThe phenomenon of overweight vehicles severely threatens traffic safety and the service life of transportation infrastructure. Rapid and effective identification of overweight vehicles is of significant importance for maintaining the healthy operation of highways and bridges and ensuring the safety of people's lives and property. With the problems of high cost and low efficiency, the traditional vehicle weighing systems can only meet some of the requirements of different scenarios. The development of artificial intelligence technologies, especially deep learning, has greatly enhanced the accuracy and efficiency of computer vision. To this end, the paper proposes a method using computer vision and deep learning for the non‐contact identification of overweight vehicles. By constructing two deep learning models and combining them with the vehicle vibration model and relevant specifications, the weight and maximum allowable weight of the vehicle are obtained to make a comparison for determining overweight. Experimental verification was performed using a two‐axle vehicle as an illustrative example, and the results demonstrate that the proposed method exhibits excellent feasibility and effectiveness. It shows significant potential in real‐world scenarios, laying a research foundation for practical engineering applications. Additionally, it provides a reference for the governance and decision‐making of overweight issues for relevant authorities.

Radar-Based Pedestrian and Vehicle Detection and Identification for Driving Assistance

The introduction of advanced driver assistance systems has significantly reduced vehicle accidents by providing crucial support for high-speed driving and alerting drivers to imminent dangers. Despite these advancements, current systems still depend on the driver’s ability to respond to warnings effectively. To address this limitation, this research focused on developing a neural network model for the automatic detection and classification of objects in front of a vehicle, including pedestrians and other vehicles, using radar technology. Radar sensors were employed to detect objects by measuring the distance to the object and analyzing the power of the reflected signals to determine the type of object detected. Experimental tests were conducted to evaluate the performance of the radar-based system under various driving conditions, assessing its accuracy in detecting and classifying different objects. The proposed neural network model achieved a high accuracy rate, correctly identifying approximately 91% of objects in the test scenarios. The results demonstrate that this model can be used to inform drivers of potential hazards or to initiate autonomous braking and steering maneuvers to prevent collisions. This research contributes to the development of more effective safety features for vehicles, enhancing the overall effectiveness of driver assistance systems and paving the way for future advancements in autonomous driving technology.

Intelligent Traffic Signal Management with PLC

This study introduces a Smart Traffic Light Control System (STLCS) that uses advanced technologies to improve traffic regulation. The key components include a camera-based traffic density detection module and a specialized vehicle identification system using Radio Frequency (RF) technology. The system’s core is an Arduino Uno board along with several converters and a Programmable Logic Controller (PLC). Field tests show that STLCS effectively analyses traffic density and prioritizes emergency vehicles, proving its potential for global traffic management improvements

Parameter identification of an open-frame underwater vehicle based on numerical simulation and quantum particle swarm optimization

Accurate parameter identification of underwater vehicles is of great significance for their controller design and fault diagnosis. Some studies adopt numerical simulation methods to obtain the model parameters of underwater vehicles, but usually only conduct decoupled single-degree-of-freedom steady-state numerical simulations to identify resistance parameters. In this paper, the velocity response is solved by applying a force (or torque) to the underwater vehicle based on the overset grid and Dynamic Fluid-Body Interaction model of STAR-CCM+, solving for the velocity response of an underwater vehicle in all directions in response to propulsive force (or moment) inputs. Based on the data from numerical simulations, a parameter identification method using quantum particle swarm optimization is proposed to simultaneously identify inertia and resistance parameters. By comparing the forward velocity response curves obtained from pool experiments, the identified vehicle model’s mean square error of forward velocity is less than 0.20%, which is superior to the steady-state simulation method and particle swarm optimization and genetic algorithm approaches.

Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition

Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates. This paper proposes a new license plate detection and recognition method based on the deep learning YOLO v8 method, image processing techniques, and the OCR technique for text recognition. For this, the first step was the dataset creation, when gathering 270 images from the internet. Afterward, CVAT (Computer Vision Annotation Tool) was used to annotate the dataset, which is an open-source software platform made to make computer vision tasks easier to annotate and label images and videos. Subsequently, the newly released Yolo version, the Yolo v8, has been employed to detect the number plate area in the input image. Subsequently, after extracting the plate the k-means clustering algorithm, the thresholding techniques, and the opening morphological operation were used to enhance the image and make the characters in the license plate clearer before using OCR. The next step in this process is using the OCR technique to extract the characters. Eventually, a text file containing only the character reflecting the vehicle's country is generated. To ameliorate the efficiency of the proposed approach, several metrics were employed, namely precision, recall, F1-Score, and CLA. In addition, a comparison of the proposed method with existing techniques in the literature has been given. The suggested method obtained convincing results in both detection as well as recognition by obtaining an accuracy of 99% in detection and 98% in character recognition.

Battery parameter identification for unmanned aerial vehicles with hybrid power system

Unmanned aerial vehicles (UAVs) nowadays are getting soaring importance in many aspects like agricultural and military fields. A hybrid power system is a promising solution toward high energy density and power density demands for UAVs as it integrates power sources like internal combustion engine (ICE), fuel cell (FC) and lowcapacity lithium-polymer (LIPO) batteries. For robust energy management, accurate state-of-charge (SOC) estimation is indispensable, which necessitates open circuit voltage (OCV) determination and parameter identification of battery. The presented research demonstrates the feasibility of carrying out incremental OCV test and even dynamic stress test (DST) by making use of the hybrid powered UAV system itself. Based on battery relaxation terminal voltage as well as current wave excitation, novel methods for OCV determination and parameter identification are proposed. Results of SOC estimation against DST through adaptive unscented Kalman filter (AUKF) algorithm show that parameters and OCV identified with longer relaxation time don’t yield better SOC estimation accuracy. Besides, it also discloses that OCV played the vital role in affecting SOC estimation accuracy. A detailed analysis is presented showing that mean discharging rate and current wave amplitude are the major factors which affect the quality of OCV identified related to SOC estimation accuracy.

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

Recent advancements in image processing and machine-learning technologies have significantly improved vehicle monitoring and identification in road transportation systems. Vehicle classification (VC) is essential for effective monitoring and identification within large datasets. Detecting and classifying vehicles from surveillance videos into various categories is a complex challenge in current information acquisition and self-processing technology. In this paper, we implement a dual-phase procedure for vehicle selection by merging eXtreme Gradient Boosting (XGBoost) and the Multi-Objective Optimization Genetic Algorithm (Mob-GA) for VC in vehicle image datasets. In the initial phase, vehicle images are aligned using XGBoost to effectively eliminate insignificant images. In the final phase, the hybrid form of XGBoost and Mob-GA provides optimal vehicle classification with a pioneering attribute-selection technique applied by a prominent classifier on 10 publicly accessible vehicle datasets. Extensive experiments on publicly available large vehicle datasets have been conducted to demonstrate and compare the proposed approach. The experimental analysis was carried out using a myRIO FPGA board and HUSKY Lens for real-time measurements, achieving a faster execution time of 0.16 ns. The investigation results show that this hybrid algorithm offers improved evaluation measures compared to using XGBoost and Mob-GA individually for vehicle classification.

Blockchain-enabled intelligent toll management system

Abstract Road toll tax contributes significantly in the economic development of any nation. In developing countries, the toll tax collection is carried out either manually or electronically. However, both approaches suffer from various challenges, including prolonged waiting times, lack of transparency, high operational costs, and concerns regarding data security and privacy. This research aims to address these challenges using a blockchain-based system. The proposed system employs advanced image processing techniques, specifically “You Only Look Once” version 5 (YOLOv5), to accurately capture and store vehicles’ registration numbers in a local server situated at toll plazas. Subsequently, the vehicle identification, along with the driver’s credentials, is transmitted to an application server, where an Ethereum smart contract verifies the information and automatically deducts the toll charges from the driver’s account. The results from this study indicate that the proposed system effectively reduces vehicle waiting time and facilitates uninterrupted vehicular movement. Additionally, the system ensures transaction transparency, safeguards the security and privacy of vehicle details, facilitates non-stop payments, rendering unnecessary cash payments or radio-frequency identification scanning at toll booths, and incorporates a decentralized architectural framework to enhance security and mitigate potential system failures.

Speed Management Blueprint: Conception of an IoT-Based Electric Vehicle Speed Limiter Monitoring System for Kigali City Vehicles

The Electric Vehicle Speed Limiter Monitoring System is an Internet of Things (IoT)-based solution to prevent traffic accidents in Kigali, Rwanda. The system utilizes advanced technology to monitor vehicle speed, identity, and position, including GPS, RFID cards, RFID 522 modules, and IR sensors. While RFID cards facilitate easy vehicle identification, RFID 522 modules identify speed infractions, IR sensors guarantee precise speed measurement, and GPS tracking offers real-time monitoring capabilities. Campaigns to raise public awareness will assist the system, which will be smoothly incorporated into Kigali’s road network and examined for patterns and trends. Ensuring seamless implementation and enforcement will require collaboration with municipal authorities, law enforcement agencies, and transportation authorities. Anticipated results include a significant decrease in traffic accidents, increased traffic safety precautions, and improved traffic management techniques. Advanced technology like IR sensors, RFID cards, RFID 522 modules, and GPS are used by Kigali, Rwanda’s IoT-based Electric Vehicle Speed Limiter Monitoring System, to track vehicle position, identification, and speed. It conducts public awareness campaigns, analyses speed-related accidents, and integrates them with Kigali’s road network. Smooth implementation is ensured by cooperation with local law enforcement and authorities, which lowers accident rates and improves traffic management techniques. The system aims to improve road safety and foster a safer atmosphere for all Kigali drivers.

Vehicle detection in varied weather conditions using enhanced deep YOLO with complex wavelet

Traffic congestion is prevalent in many major and medium-sized cities throughout different countries in contemporary society. In traffic images, various multi-sized vehicles are tightly clustered together and obstructed from one another. Identifying vehicles in such instances is crucial for urban traffic surveillance, safety monitoring, and legal concerns but it also presents major challenges. The remarkable detection accuracy and efficiency of deep learning-based systems have led to their recent and extensive use in vehicle identification. There are significant advanced YOLO models with different backbone architectures and frameworks developed for vehicle detection. Yet, the performance of YOLO variants are facing the challenges of handling false detection against occluded and densely sophisticated scenarios. The proposed model is developed to address such types of limitations, for example; dynamic illumination, noisy images, and scale sensitivity to improve the vehicle detection rate in different traffic scenarios and varying weather conditions. The proposed study employs an improved YOLOv4 to identify moving vehicles in different lighting conditions including daylight, cloudy, rainy, and night. For hybridization, three techniques are utilized such as the Multiscale Retinex, Dual tree complex wavelet transform (DTCWT), and Pulse Coupled Neural Networks (PCNN). The DTCWT is employed for multiscale decomposition and to denoise the complex high frequency subband information, then the denoised subbands are reconstructed into a denoised image. The Multiscale retinex is utilized to reduce the halo artifacts on high-contrast edges and maintain the balance with dynamic range compression and color reproduction. The synchronizing pulse burst property of PCNN is used to detect the isolated noisy pixels and modify the detected noisy pixels. From the results it is worth noting that the developed model surpasses state-of-the-art methods in sunny, night, cloudy, and rainy modes. The proposed method using the DTCWT technique can detect the vehicles with mAP of 91.09% and 35FPS.

Enhanced Vehicle Identification: A Machine Learning Approach to Number Plate Recognition

Enhanced Vehicle Identification: A Machine Learning Approach to Number Plate Recognition

Traffic Monitoring and Signal Controlling using RFID for Emegency Vehicles

Abstract: This paper presents the concept, Traffic Monitroting and signal controlling using RFID technology to prioritize ambulance passage during emergencies. Traffic lights are crucial for managing the flow of vehicles on the road. In particular, when there are emergencies, the traffic situation is getting worse. Emergency vehicles find it challenging to cross busy road during periods of traffic congestion. A medical emergency vehicles like ambulances, or victims of accidents must be transferred to it as soon as possible. They could be late for rescue efforts because of traffic signals. Identification of emergency vehicles, control of traffic lights, and provision of a clear path for their travel are the objectives of our paper. The main element of this system that implements the control system is radio frequency Identification (RFID). Through RFID detection, the system identifies the presence of an ambulance approaching any lane and promptly initiates a sequence wherein traffic lights for the ambulance's intended lane turn green, while simultaneously halting traffic flow in other lanes. By integrating RFID technology into the traffic management infrastructure, the prototype ensures swift and efficient response to emergency situations, minimizing delays in medical assistance. The system's intelligent algorithm enables it to override regular traffic light patterns, granting immediate passage to ambulances without compromising safety or causing disruptions to traffic flow in adjacent lanes. This approach enhances emergency response capabilities, optimizing the delivery of critical healthcare services and potentially saving lives in urgent medical situations. The system's ability to dynamically adapt traffic signals based on real time ambulance detection underscores its potential to revolutionize emergency response protocols, contributing to enhanced public safety and well-being in densely populated urban areas. The RFID tag helps to verify that emergency vehicles The emergency vehicle can clear all junctions without wasting time at the traffic signals this keeps happening until the ambulance arrives at its destination.

Research on the Expansion Application of Electronic Toll Collection System

Electronic Toll Collection (ETC) system is an important part of modern intelligent transport system, which plays an important role in intelligent car parks and congestion charging. Electronic Toll Collection (ETC) system includes automatic vehicle identification system and electronic billing system. The automatic vehicle identification system includes three parts: automatic vehicle identification (AVI), automatic vehicle model identification (AVC) and inspection system (ES).The application of ETC in intelligent car parks and congestion charging also includes automatic vehicle identification system (automatic vehicle identification, automatic vehicle model identification and inspection system) and electronic billing system. In intelligent car parks, ETC technology achieves fast and accurate automatic toll payment by automatically identifying the ETC tags carried by vehicles, which greatly improves the efficiency of car parks and reduces the queuing and waiting time due to manual toll collection. In terms of congestion charging, ETC technology provides an effective means to implement congestion charge collection. By setting up ETC toll lanes in core urban areas or busy road sections, the system can automatically record the information of the vehicles passing through and carry out toll settlement, thus realising dynamic toll collection according to the degree of congestion and the frequency of vehicle use. This congestion charging strategy aims to regulate urban traffic flow through economic means, alleviate traffic congestion and promote the sustainable development of urban transport. Although China's theoretical research on congestion charging, has been relatively mature, but affected by a variety of factors, no city has yet to start the implementation of congestion charging, the lack of experience in practice. Singapore will not stop the car charging system (ETC) technology successfully applied to urban congestion charging and intelligent car parks, ETC system in intelligent car parks and congestion charging application, help to promote the development of urban transport to a more intelligent, green direction. China can learn from the successful experience of foreign countries and apply it to its own intelligent car parks and congestion charging. This paper introduces the basic principle of ETC, discusses the research progress of ETC in terms of technical characteristics, and analyses the application scenarios of ETC in intelligent car parks and congestion charging. By examining the opportunities offered by Electronic Toll Collection (ETC) systems in smart car parks and congestion pricing, this study aims to delve into the transformative impacts of Electronic Toll Collection (ETC) systems on intelligent mobility and contribute to the sustainable development of intelligent mobility.

Enhancing Automatic Vehicle Number Plate Recognition Systems: A Multidimensional Approach to Improve Efficiency and Applicability

Real Time Number Plate Recognition System is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. No plate help for movement of traffic securely without any collision. They can reduce the number of accidents on roads like pedestrian accident and right-angle collision of two cars. Number plate recognition (NPR) can be used in various fields such as vehicle tracking, traffic monitoring, automatic payment of tolls on highways or bridges, surveillance systems, tolls collection points, and parking management systems. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is localized using Neural Network(rcnn) then image segmentation is done on the image. Character recognition technique is used for the character extraction from the plate. The system is implemented and simulated in python, and its performance is tested on real image. Automatic vehicle 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 vehicle detection and recognitions but they are not very useful for parking system. In this project we aim to make an application which will help for society and mostly for corporation buldings in each state for doing their work very efficiently and in very small time. Live camera feeds are the primary input we give the system. On the main gate is a camera. When a car or bike passes in front of the camera, the camera reads the number plate number. Using an OCR algorithm, this vehicle can be determined to be authorized or not. If the car is not allowed in the premises the notification will be activated. authorized popup will be on if the vehicle is authorized

A DEEP LEARNING POWERED SMART PARKING SYSTEM BASED ON FACIAL RECOGNITION AND LICENSE PLATE ANALYSIS

In today's urban landscapes, parking congestion has become a pervasive challenge, leading to wasted time, increased emissions, and driver frustration. The ever-growing urban population, coupled with the increasing number of vehicles, has led to a severe shortage of parking spaces in metropolitan areas. This scarcity has not only made finding a parking spot a time-consuming and frustrating experience but has also contributed to traffic congestion and environmental pollution. Traditional parking management systems, which often rely on manual ticketing or physical barriers, are becoming inadequate in addressing these challenges. To address this issue, innovative solutions that combine technology and automation are on the rise. Traditional parking management systems often rely on physical infrastructure and human intervention, resulting in inefficiencies and limited scalability. In contrast, the integration of deep learning techniques and advanced computer vision technology into parking management opens up new possibilities for a smarter, more efficient, and user-friendly experience. This system leverages two key components: facial recognition technology to identify vehicle occupants and automatic recognition of license plate numbers for vehicle identification. By seamlessly integrating these technologies, the system not only facilitates effortless parking but also enhances security and optimizes parking space utilization.we will explore the fundamental components, benefits, and potential impact of the Face and Number Plate-Based Smart Parking System. Experimental results shows that improved efficiency in smart parking system using face and number plate verification system.