Developing an Advanced Automated Parking System using Computer Vision

Local Chapter Fez, Morocco Chapter

Coordinated byMorocco ,

Status: Ongoing

Project background.

Morocco, a diverse and culturally rich country in North Africa, experiences a growing demand for efficient parking solutions, particularly in urban areas with bustling commercial centers, tourist attractions, and business districts. As Morocco attracts both local residents and international visitors, the need for an advanced parking system that caters to various license plates languages, such as Arabic, French, and potentially English, becomes evident.

Traditional parking management often involves manual ticketing, which can lead to congestion, inefficiencies, and security concerns. To address these challenges and provide a seamless parking experience, the introduction of an Automated Parking System with Multilingual License Plate Detection in Morocco is proposed

The problem.

Developing an advanced automated parking system for a large shopping mall or office complex that can efficiently manage parking spaces and handle the entry and exit of vehicles without the need for human intervention. The goal is to create a system that supports multiple languages, including Arabic, English, and French, for license plate detection. The system uses computer vision technology with YOLOv8-based license plate detection and an OCR module for character recognition.

How it Works:

1. Entrance Monitoring:
As a vehicle approaches the entrance, a camera equipped with YOLOv8-based license plate detection is installed. The camera continuously captures live video footage and processes it in real-time using the YOLOv8 model. When the model detects a car within the frame, it focuses on the area where the license plate is expected to be located.

2. Multilingual License Plate Detection:
The YOLOv8 model is trained on a diverse dataset containing license plates from different regions and languages, including Arabic, English, and French. This enables the model to recognize and locate license plates accurately in various languages and fonts.

3. Character Recognition for Multilingual Support:
Once the license plate is detected, the system extracts the characters from the plate. The character recognition module (OCR) is trained on a diverse dataset encompassing Arabic, Latin (English), and French characters to accurately recognize and convert the license plate numbers to text.

4. Language Detection and Translation:
To enhance user experience, the system incorporates language detection to identify the language of the license plate automatically. If the detected language is different from the user interface language, the system can provide real-time translations of the license plate numbers and parking-related information into the user’s preferred language.

5. License Plate Validation and Entry:
The recognized license plate number is checked against the database of registered vehicles, which can accommodate multilingual information. If the plate number matches with an authorized vehicle, the parking barrier automatically opens to allow the vehicle to enter. If the plate number is not registered or matches a blacklisted vehicle, the barrier remains closed, and a security alert is triggered.

6. Parking Space Management:
As the vehicle enters the parking lot, the system updates the available parking spaces count based on the vehicle’s size and allocates an appropriate parking spot for it. Real-time records of available parking spaces are maintained in the multilingual database.

7. Exit Monitoring and Payment:
At the exit, another camera with YOLOv8-based license plate detection verifies the vehicle’s license plate as it approaches the exit. The system checks the recognized plate number against the database to verify if the vehicle has paid for parking or has overstayed its allotted time. If necessary, the system can integrate with payment gateways to automatically charge the parking fee.

Project goals.

- Multilingual License Plate Detection: The primary goal of the project is to develop a robust automated parking system capable of accurately detecting license plates in multiple languages, including Arabic, French, and English. The system should be able to handle variations in languages, fonts, and designs commonly found on license plates in Morocco.- Real-Time Processing: The project aims to optimize license plate detection and character recognition algorithms to achieve real-time processing of live video feeds. The system should efficiently process incoming vehicles, verify their license plate information, and make prompt decisions for entry and exit authorization.- User-Friendly Interface: The project seeks to design an intuitive and multilingual user interface that caters to the diverse linguistic backgrounds of the users. The interface should provide real-time translations and be easy to navigate, ensuring a convenient and inclusive parking experience for all visitors.- Parking Space Management: The automated parking system should effectively manage parking spaces by accurately allocating spots based on vehicle size and availability. The project aims to optimize parking space utilization and provide real-time data on available spaces.- Enhanced Security: The project emphasizes enhancing security by accurately identifying authorized vehicles and detecting unauthorized or suspicious entries. By implementing effective license plate detection, the system aims to contribute to a safer parking environment.

Project plan.

  • Week 1

    Gather Data: Collect a diverse dataset of license plate images in Arabic, French, and English from various regions in Morocco. Annotate the dataset with bounding boxes around license plates and corresponding text labels.

    Setup Environment: Set up the development environment with necessary libraries and frameworks for computer vision and deep learning, such as TensorFlow or PyTorch.

    Pretrained Model Exploration: Research and experiment with pretrained YOLOv8 models for object detection. Choose the most suitable model for license plate detection and modify it for multilingual support.

  • Week 2

    Data Preprocessing: Preprocess the collected dataset to ensure uniformity in size, format, and resolution. Augment the dataset to increase its diversity and enhance model generalization.

    Model Training (License Plate Detection): Train the modified YOLOv8 model on the preprocessed dataset for license plate detection. Fine-tune the model to achieve high accuracy for Arabic, French, and English license plates.

  • Week 3

    Character Recognition Model: Develop an OCR system capable of recognizing characters from multiple languages. Train the OCR model on a diverse dataset containing Arabic, French, and English characters. Integration and Testing: Integrate the license plate detection and OCR modules to create a complete pipeline for license plate recognition. Test the system on sample images and video streams to verify its performance.

  • Week 4

    User Interface Design: Design and develop a user-friendly interface for the parking system. Implement real-time language detection and translation to accommodate multilingual users. Payment System Integration: Integrate the parking system with payment gateways to enable seamless payment processing for parking fees.

  • Week 5

    Optimization: Optimize the system for real-time performance, considering hardware acceleration and model quantization techniques. Conduct profiling and fine-tuning to achieve efficient processing. Security and Data Privacy: Implement data security measures to protect license plate information and user data. Ensure compliance with local regulations and privacy laws

  • Week 6

    Adaptability Testing: Test the system’s adaptability by deploying it in different parking lots with varying conditions and license plate designs. Gather feedback and make necessary adjustments. Documentation: Prepare comprehensive documentation, including system architecture, model details, deployment instructions, and user guides.

  • Week 7

    Full Deployment: Deploy the Multilingual Automated Parking System at selected pilot locations in Morocco. Monitor its performance and address any issues that arise.

  • Week 8

    Final Testing and Validation: Conduct extensive testing and validation of the system’s performance in real-world scenarios. Fine-tune the system based on user feedback and validation results. Project Presentation and Handover: Present the project’s outcomes, learnings, and achievements to stakeholders. Handover the fully functional system to the designated operations team for maintenance and further expansion.

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