CHAPTER 13 AI BASED VEHICLE (NUMBER PLATE DATA) RECOGNITION SYSTEM

In the era of rapid technological advancement, artificial intelligence (AI) has emerged as a transformative force across various domains, revolutionizing conventional processes and enhancing efficiency. One such area witnessing remarkable innovation is the field of transportation, where AI-powered solutions are reshaping the landscape of vehicle recognition systems. Among these, AI-based vehicle number plate recognition systems stand out as a pioneering application with multifaceted benefits and implications.

Vehicle number plate recognition, also known as Automatic Number Plate Recognition (ANPR) or License Plate Recognition (LPR), refers to the automated detection and interpretation of vehicle license plates through the utilization of AI algorithms and computer vision techniques. By harnessing the power of machine learning, neural networks, and image processing, these systems can accurately extract alphanumeric characters from license plates, decode them, and subsequently analyze the acquired data for diverse purposes.

The significance of AI-based vehicle number plate recognition systems transcends mere identification; it extends to critical functionalities across various sectors. In law enforcement, such systems play a pivotal role in enhancing public safety and security by enabling swift and accurate identification of vehicles involved in criminal activities, traffic violations, or Amber Alerts. Moreover, in toll collection and parking management, ANPR systems streamline operations, facilitate seamless transactions, and mitigate revenue leakages by automating the process of fee collection and vehicle tracking.

Furthermore, the integration of AI-driven insights from vehicle number plate data holds immense potential in facilitating urban planning, traffic management, and transportation analytics. By analyzing patterns in vehicle movements, congestion hotspots, and commuting behaviors, city authorities can optimize infrastructure planning, improve traffic flow, and mitigate environmental impacts.

However, the deployment of AI-based vehicle number plate recognition systems also raises pertinent considerations regarding privacy, data security, and ethical usage. Striking a balance between the benefits of enhanced surveillance capabilities and the protection of individual liberties remains a critical challenge in the adoption and regulation of such technologies.

In this context, this paper aims to explore the intricacies of AI-based vehicle number plate recognition systems comprehensively. By delving into the underlying technologies, applications, challenges, and ethical considerations, we seek to elucidate the transformative potential of these systems while addressing the imperative of responsible deployment and governance in a rapidly evolving technological landscape.

Code:

import cv2 import csv from datetime import datetime import pytesseract # Load pre-trained Haarcascades for license plate detection plate_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_russian_plate_number.xml') # Initialize video capture from the inbuilt camera (0 for default camera) cap = cv2.VideoCapture(0) # Create and open a CSV file to store number plate data along with date/time csv_file = open('number_plate_data.csv', 'w', newline='') csv_writer = csv.writer(csv_file) csv_writer.writerow(['Date', 'Number Plate']) while True: # Read frame from camera ret, frame = cap.read() if not ret: print("Failed to capture image") break # Convert frame to grayscale gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Detect license plates in the grayscale frame plates = plate_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5) # Process detected license plates for (x, y, w, h) in plates: # Extract the number plate region from the frame plate_roi = gray[y:y+h, x:x+w] # Perform OCR (Optical Character Recognition) on the plate region plate_text = pytesseract.image_to_string(plate_roi, config='--psm 8') # Adjust psm value based on your image # Get the current date and time current_date = datetime.now().strftime('%Y-%m-%d %H:%M:%S') # Save the number plate data along with the date/time to the CSV file csv_writer.writerow([current_date, plate_text.strip()]) # Draw rectangle around the detected license plate cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2) # Display the frame with detected license plates cv2.imshow('License Plate Detection', frame) # Check for key press, if 'q' is pressed, exit the loop if cv2.waitKey(1) & 0xFF == ord('q'): break # Release video capture, close CSV file, and close all OpenCV windows cap.release() csv_file.close() cv2.destroyAllWindows()

This code utilizes Tesseract OCR to extract the text from the detected number plate region. The extracted text is then written to the CSV file along with the date/time stamp. Make sure to adjust the Tesseract configuration (config) parameters based on your specific requirements and the quality of the input images.

If you want to integrate OCR (Optical Character Recognition) to extract the actual number plate data, you can use the Tesseract OCR engine along with the pytesseract library. Make sure you have Tesseract installed on your system. You can install pytesseract using pip:

pip install pytesseract

Output:

The CSV file named number_plate_data.csv will be created in the same directory where your Python script is located. If you run the Python script in a directory, the CSV file will be generated in that directory.

After running the script, you can navigate to the directory where your Python script is located, and you should find the number_plate_data.csv file there. You can open this CSV file using any text editor or spreadsheet software like Microsoft Excel to view its contents.