Realtime Under Vehicle 3d Inspection and database management

Documentation for Real-time Under Vehicle 3D Inspection System

1. Problem Statement

In many security-sensitive areas, such as airports, military bases, government buildings, and critical infrastructure facilities, ensuring the security of vehicles entering the premises is crucial. Traditional manual inspection methods are time-consuming, inconsistent, and prone to human error. This project aims to develop a Real-time Under Vehicle 3D Inspection System using a Ryzen NPU (Neural Processing Unit) to automate and enhance the inspection process. The system will capture and analyze 3D images of a vehicle's undercarriage, detecting any potential threats or anomalies.

2. Objectives

• Automated Inspection: Automate the process of under-vehicle inspection, reducing the need for manual checks.
• Real-time Analysis: Provide real-time 3D visualization and analysis of the undercarriage to identify potential threats.
• Database Management: Maintain a database of scanned vehicles for historical reference and threat detection.
• High Accuracy: Utilize advanced image processing and machine learning techniques to achieve high accuracy in anomaly detection.
• Scalability: Design the system to be scalable and adaptable to different environments and security levels.

3. Importance

• Enhanced Security: Reduces the risk of human error and improves the thoroughness of inspections.
• Efficiency: Significantly reduces inspection time, allowing for a smoother flow of vehicles into secured areas.
• Data-Driven Decision Making: Provides a reliable data record for security audits and incident investigations.
• Cost-Effective: Reduces the need for extensive human resources and provides long-term savings.

4. Necessary Software and Hardware Requirements

Hardware Requirements

• Ryzen NPU: For real-time processing and analysis.
• 3D Camera/Scanner: To capture high-resolution images of the vehicle's undercarriage.
• Server/Workstation: To store and manage the database and run the analysis software.
• Network Equipment: For data transfer and remote monitoring.

Software Requirements

• Operating System: Linux-based (e.g., Ubuntu) for server and workstation.
• Programming Languages: Python, C++.
• Frameworks/Libraries: TensorFlow/PyTorch (for machine learning), OpenCV (for image processing), Open3D (for 3D data handling).
• Database Management: PostgreSQL or MySQL for managing inspection data.
• Web Server: Apache/Nginx for hosting the monitoring and reporting interface.
• Code Editor/IDE: Visual Studio Code, PyCharm.

5. Methodology

5.1. System Setup

1. Hardware Installation:

o Set up the Ryzen NPU, 3D cameras, and network equipment.

o Install the vehicle platform for consistent positioning.

2. Software Installation:

o Set up the operating system and necessary software.

o Configure the database and web server.

5.2. Data Collection

• Step 1: When a vehicle arrives, it is guided onto the platform.
• Step 2: The 3D scanner captures a high-resolution image of the vehicle's undercarriage.
• Step 3: The data is sent to the server for processing.

5.3. Data Processing

• Step 1: The captured 3D images are preprocessed using OpenCV for noise reduction and normalization.
• Step 2: The cleaned data is then analyzed using a machine learning model trained to detect anomalies.
• Step 3: The output is a visual representation and a list of potential threats, if any.

5.4. Database Management

• Step 1: The system stores all inspection data, including images and analysis results, in the database.
• Step 2: Historical data is available for security audits and comparison against future inspections.

5.5. User Interface

• Step 1: A web-based interface displays real-time inspection results.
• Step 2: Users can search and view past inspections, export reports, and manage system settings.

5.6. Code Snippets

5.6.1. Data Capture and Preprocessing

python

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import cv2

import numpy as np

# Capture image

camera = cv2.VideoCapture(0)

ret, frame = camera.read()

camera.release()

# Preprocessing

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

blurred = cv2.GaussianBlur(gray, (5, 5), 0)

# Save preprocessed image

cv2.imwrite('preprocessed_image.jpg', blurred)

5.6.2. Anomaly Detection

python

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import tensorflow as tf

from tensorflow.keras.models import load_model

# Load pre-trained model

model = load_model('anomaly_detection_model.h5')

# Predict anomalies

image = cv2.imread('preprocessed_image.jpg')

image = cv2.resize(image, (224, 224))

image = np.expand_dims(image, axis=0)

prediction = model.predict(image)

# Analyze results

if prediction[0][0] > 0.5:

print("Anomaly Detected!")

else:

print("No Anomaly Detected.")

5.6.3. Database Management

python

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import psycopg2

# Connect to the database

conn = psycopg2.connect(

dbname="inspection_db",

user="user",

password="password",

host="localhost",

port="5432"

)

# Store data

cur = conn.cursor()

cur.execute(

"INSERT INTO inspections (date, image_path, result) VALUES (%s, %s, %s)",

('2024-07-31', 'preprocessed_image.jpg', 'No Anomaly Detected')

)

conn.commit()

cur.close()

conn.close()

Conclusion

The Real-time Under Vehicle 3D Inspection System offers a modern, automated solution for secure vehicle inspection. By leveraging advanced technologies, it ensures high accuracy, efficiency, and reliability. This documentation provides a comprehensive guide to setting up, using, and maintaining the system.