IPS: Intelligent Parking System Using YOLO and Image Processing

Parking information system with artificial neural network

This paper explains the design and fabrication of an intelligent, user-friendly parking system developed in the United Arab Emirates. The need for a smart parking system was assessed by conducting a survey on the current parking issues. This paper elaborates on the hardware and software realization of the proposed parking system developed for motorists to locate vacant parking using mobile application. The various types of vehicle detection sensors available in the market have been evaluated for the implementation of the system. The main objective of this paper is to build a prototype intelligent parking system with maximum accuracy. The goal of the project was to control the detection modules wirelessly through a customized mobile application, allowing ease of operation and maintenance. While the users can enjoy better comfort and safety, the focus is to create a self-reliable, and ecologically sustainable system while reducing searching time, fuel wastage resulting in lower carbon footprint. The mobile application has been developed using Android Studio, and the results are presented in this paper.

In view of the intelligent demand of modern large-scale parking lot and based on the enormous application advantages of fog computing on the Internet of things and the unique characteristics of NB-IoT technology, the design and application of NB-IoT technology which is based on fog computing in parking system model architecture is proposed, to promote parking systems to be more intelligent, increase efficiency, reduce expenses, and increase efficiency. The system model design adopts four-layer structure: Perception Layer, Network Layer, Cloud-Fog Platform Layer and User Layer, and uses the technical characteristics and advantages such as Fog Computing and NB-IoT to make data analysis, storage, calculation, exchange and communication in the system more integrated, fluent, and real-time. Meanwhile, the model is built into a low-latency, wide-coverage, highly intelligent parking system, and its research results provide totally new ideas and solutions to the drawbacks of the existing parking systems.

This chapter discusses the role of cloud computing and intelligent parking systems in sustainable smart cities, addressing challenges like traffic congestion, pollution, and resource inefficiency. These technologies enhance urban mobility, reduce environmental impact, and improve quality of life in cities facing rapid urbanization worldwide. This chapter offers a thorough analysis of the integration of cloud computing and intelligent parking systems in sustainable urban development, highlighting successful implementations and lessons learned. It also explores potential future developments and policy considerations to facilitate widespread adoption of these technologies, highlighting the importance of global best practices.

With the development of urbanization, the number of car holdings is growing dramatically and the problem of parking difficulties is becoming more and more prominent. The irrational distribution of vehicles and parking measures in today's cities not only wastes limited road resources, but also brings great challenges to urban traffic management. Therefore, the demand for a smarter parking system is increasing to provide more convenient services and reduce the loss of human and material resources. The IoT-based intelligent parking system adds the functions of parking reservation and real-time detection to the original parking system, which realises the visualisation of parking information and makes it more obvious to see the real-time situation of the car park, thus making parking more convenient and quicker.

This paper proposes a robust design of Hybrid Fuzzy Controller for speed and steering angle control in an Intelligent Autonomous Parking System (IAPS). The Hybrid Fuzzy Controller consists of a Base Fuzzy Controller (BFC) and a Supervisory Fuzzy Decision Tree Controller (SFDTC). The BFC evolves from previous work on fuzzy logic control for unmanned parking and it ensures that optimal parking trajectory is achieved with minimal computational cost. SFDTC further increases the system robustness when there is noise in the operating environment. The design of SFDTC combines Decision Tree theory and fuzzy inference mechanism. A data training process is also formulated to achieve better control performance. As a result, IAPS equipped with the new Hybrid Fuzzy Controller with Fuzzy Decision Tree (HFC-FDT) demonstrates optimal performance with faster convergence and minimal deviation from optimal parking trajectory. The detailed design of Supervisory Fuzzy Decision Tree Controller is presented in this paper with a MATLAB simulated experiment which concludes the superior performance of the new design.

Dynamic pricing and reservation for intelligent urban parking management

The city's efforts to improve traffic make a big step forward when license plates can be read. It explains how an intelligent transportation system should work and what steps should be taken for it to be successful. Because the number of cars is growing so quickly, ANPR is a must-have for managing traffic control. Automatic number plate recognition (ANPR) is mostly used to keep an eye on traffic and keep people safe. Number plate recognition uses image processing and the most recent advances in technology to automatically read the characters on a vehicle's license plate. In the last few years, there have been a number of technological advances in the field of studying how to read license plates. Image processing techniques like OCR make it possible for traffic surveillance to solve a wide range of problems that come up during criminal investigations, the collection of tolls, the monitoring of traffic, the regulation of speed, and the management of parking, among other things. If you want to control traffic and keep an eye on a large number of people in a transportation system, you need an ANPR system. Image processing techniques and the collection of photos of vehicles for use in the dataset have made it possible to keep an eye on traffic on a large scale. Automatic License Plate Reader (ANPR) is a way to take the steps needed to run a good intelligent transportation network. Traffic control has become an absolute must because the number of cars has grown so quickly. The main goal of ANPR is to keep an eye on and record traffic for defense purposes. To read the text on license plates, number plate recognition uses image processing, optical character recognition (OCR), and edge detection technology. The model is made up of three different parts called, respectively, the module for car detection, the module for license plate segmentation, and the module for recognition. Image processing made us more determined to stop a wide range of illegal activities, such as armed robberies of cars, breaking traffic rules, and the way law enforcement is handled. This review study looked at all of the different designs for reading license plates that have been put into use so far.

Short- and long-range wireless communication is imperative for many remote monitoring applications such as industrial automation, digital health, and smart cities. Remote monitoring is one of the components of complex systems that can be optimized with the use of machine-learning-based techniques. In this article, an intelligent parking system is presented. The system exploits the benefits of a synergy between licensed and unlicensed wireless technologies, IoT, computer vision, and artificial intelligence. We discuss how an end user can interact with the system and manage it with a user interface. The user interface provides a range of dashboard facilities that provide various statistics about car park utilization and optimization facilities. Our proposed solution comprises a range of sensors including high-resolution cameras, which are distributed throughout a car park. The cameras are connected using different types of wireless communication. The data is processed in real time by the sensors and sent to a cloud server, where it is analyzed, stored, and presented to the end user. The object detection, tracking, and optical character recognition system are executed by the vision sensor, which utilizes a combination of convolutional neural networks and a proprietary deep-learning-based object detection algorithm. This solution has achieved high levels of classification accuracy (99.97 percent) and high image processing speeds (66 ms).

Parking and running red lights are the major problems in traffic violations. This study presents two application systems for finding parking spaces and controlling traffic lights. An intelligent traffic light system adaptively controls the time of traffic lights so that the period of green lights is adjusted according to the real-time traffic flow. The traffic jam is thus effectively reduced. An intelligent parking system is also implemented to help drivers find parking spaces efficiently. Initially, a web camera is utilized to capture images. Hence, vehicles are identified to compute the remaining number of parking spaces. A website shows the remaining parking spaces. This system can help a user to find suitable parking spaces quickly. Therefore, parking violations are reduced. The YOLO (You Only Look Once) neural network is employed to identify vehicles, where the networks are trained through transfer learning. Only a small quantity of training images is required. The experimental results show that the vehicle identification accuracy rate can reach 93%, the recall rate reaches 83%, and the overall F measure reaches 87.7%. Therefore, the vehicle identification system can be applied to intelligent traffic light control and roadside parking spaces.

The rapid growth of modern cities has significantly increased vehicle numbers, exacerbating parkingrelated challenges in traffic control. Drivers frequently spend excessive time searching for parking spaces, causing inconvenience, worsening congestion, and increasing environmental pollution. According to IBM research, approximately 30% of urban traffic congestion stems from parking searches, highlighting the severe impact of parking issues on urban life. This paper explores UX/UI design principles in developing web applications for intelligent parking systems, emphasizing their importance in addressing these challenges. Web-based intelligent parking systems provide real-time parking information and allocation, reducing congestion and emissions while enhancing driver convenience. Effective design aims to create intuitive, user-friendly interfaces, incorporating interactive city maps, dynamic markers, simplified parking allocation, secure payment systems, and robust data protection. Integrating navigation with public transport further enriches the user experience by offering optimal routes. As urbanization and vehicle numbers surge globally, optimizing parking processes is essential to alleviate congestion, reduce pollution, and minimize driver stress. Advances in technologies like sensors and real-time systems enable efficient parking solutions. Additionally, adherence to advanced UX/UI standards ensures user satisfaction, promoting the widespread adoption of these technologies. The creation of secure, intuitive parking applications is crucial for improving urban environments and traffic efficiency.

Intelligent parking systems have been recognized as a core technological intervention for resolving parking garage shortages and advancing traffic safety. Nevertheless, it remains challenging to generate a smooth, accurate, and optimal parking trajectory when employing conventional intelligent path optimization algorithms. Hence, building upon a newly designed optimization model for intelligent vehicle parking path planning, this study develops an improved immune moth-flame optimization algorithm (IIMFO). Specifically, aiming at the shortest path length and smooth enough trajectory, we leverage a cubic spline interpolation-driven path planning model to resolve the complex automatic parking trajectory optimization problem. To significantly strengthen the optimization effect, we introduce immune concentration selection, nonlinear decaying adaptive inertia weight adjustments, and elite opposition-based learning mechanisms to improve the immune moth-flame algorithm. Based on the evaluation results of the test functions, as well as the simulation and semi-automatic experiments of the real-world scenario of intelligent vehicle parking path optimization, the results indicate that the improved strategy can achieve better parking trajectories.

Intelligent transportation systems technologies are being developed and applied through transportation systems in the United States. An example of this type of innovation can be seen on toll roads where a driver is required to deposit a toll in order to drive on a toll road. To automatically process toll services, automatic vehicle identification (AVI) technologies should be developed and implemented. A summary is presented of a study that focused on the performance analysis of three types of AVI technologies that could be used for toll collection applications on the Crosstown Expressway and the Veterans Expressway in Hillsborough County, Florida. The three AVI technology types were optical/laser scanner, radio frequency, and inductive loop. The study aimed at analyzing the total average delay and AVI market share due to the usage of AVI technologies. Delay performance and market share resulting from the application of each AVI technology type were used to compare the effects of different AVI technology types. The analysis was based on computer simulation using an AVI lane choice model (demand model) and a delay model (performance model) with the data collected from the two toll roads in Hillsborough County. From the simulation results, a cost-benefit analysis was performed to evaluate the impacts of AVI technologies on the ratio of benefit over cost. Results of the cost-benefit analysis can be used to determine the optimal configuration of AVI lanes and manual lanes. A procedure is provided that can be used for planning and designing AVI technologies used in toll service.

Traditional license plate recognition technology mostly uses traditional image processing methods to find out the characteristics of the license plate, and then crop and recognize the characters. The process needs to be modified due to the different environments, scenes and conditions. In recent years, many studies have implemented license plate and character recognition by using deep learning algorithms. Although it has a good recognition accuracy, the calculation speed still cannot reach the level of real-time recognition. This research proposes a real-time license plate recognition system based on YOLOv3, which uses deep learning model to realize the vehicle license plate recognition, lane identification and vehicle trajectory tracking. In this study, a web-based platform is established to present the result of license plate recognition and trajectory, and the streaming roadside video in the campus. In the platform, license plates of driving vehicles can be identified in real-time, and the user can search and track specific vehicle intuitively. In the experiment, the average accuracy of the system performs 84.3% in real-time license plate recognition, and 100% in lane identification. The system can process in 40 FPS, which can meet the level of real-time system. In the future, the system can cooperate with traffic access control in campus or community to improve the efficiency of traffic control.

India is the sixth largest economy in the world. With the rise in its population, urbanization has become a global phenomenon. Due to this, there has been a rapid increase in the number of vehicles on the road. However, modern infrastructure is failing to provide secure and efficient parking system for these increased vehicles. The problems arise because of the limited parking spaces. The increasing vehicles on the road along with the improper management of the available parking space lead to the parking-related problems as well as an increased traffic jam in the urban areas. Thus, it is highly necessary to develop a smart parking system. This paper involves a parking system that works on automatic number plate recognition (ANPR) algorithm using optical character recognition (OCR). The system consists of a camera at the entrance of the parking gate, which will be used for number plate recognition using digital image processing, and then a parking slot will be allocated depending on the status of the vehicle. Also at the exit, the vehicle number is identified and the information is updated to the cloud using Internet of things (IoT) where it can be monitored. The availability of the parking slots can also be viewed on the mobile through the android app created for ease of public usage. This paper makes excellent use of all the available resources in order to create an efficient solution for the existing situation.