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AI-Powered ANPR Traffic Management in Singapore

Industry

Smart Mobility

Duration

2 months

Team Size

4 people

Project Overview

A group responsible for managing vehicular traffic in Singapore approached Katomaran Technologies with a clear goal: to modernize their operations. Their existing system was outdated, cumbersome, and struggling to keep up with the demands of a fast-paced urban environment. They needed an AI-powered Automatic Number Plate Recognition (ANPR) solution capable of reading license plates accurately in any weather—blazing sun, heavy rain, or dark nights. The new system also had to work smoothly with their current citywide CCTV network without requiring a major infrastructure overhaul, all while keeping privacy front and center.

Challenges

Making traffic management effortless wasn't as simple as it sounds

  • 1
    Weather Variability: The system needed to accurately read license plates in diverse weather conditions, including intense sunlight, heavy rain, and low-light night environments.
  • 2
    CCTV Integration: The solution had to integrate seamlessly with Singapore's existing citywide CCTV network without requiring costly or disruptive infrastructure changes.
  • 3
    Privacy and Data Security: Ensuring the protection of sensitive data, particularly license plate information tied to individuals, was critical to comply with privacy regulations.

Our Solution

We tackled these challenges with smart, practical solutions

  • 1
    AI-Powered ANPR for All Conditions: Developed a robust AI-based Automatic Number Plate Recognition (ANPR) system trained on a vast dataset of images from various weather scenarios to ensure high accuracy in any condition.
  • 2
    Seamless CCTV Integration: Designed an integration layer using RESTful APIs to connect the ANPR system with the existing CCTV network, enabling real-time data processing without hardware upgrades.
  • 3
    Privacy-Focused Security Measures: Implemented end-to-end encryption, data anonymization, and strict access controls to safeguard sensitive information and ensure compliance with Singapore's privacy laws.

Key Modules

The system keeps everything running smoothly

  • 1
    ANPR Engine: The core component responsible for detecting and reading license plates from CCTV footage with high precision.
  • 2
    Weather Adaptation Module: Adjusts image processing techniques in real-time to account for varying weather conditions, ensuring consistent performance.
  • 3
    Integration Layer: Facilitates smooth communication between the ANPR system and the city's existing CCTV infrastructure via APIs.
  • 4
    Privacy Compliance Module: Enforces security protocols, including encryption and access management, to protect data and meet regulatory standards.
  • 5
    Dashboard and Reporting Tool: Provides a user-friendly interface for traffic management teams to monitor operations, generate reports, and gain insights.

Services Offered

Services that we offered to make the client vision ourreality

Web Application Development

Crafting secure, scalable platforms that power your online presence.

AI & ML

Harness the power of data with custom models and intelligent automation to drive smarter, faster decisions.

DevOps & Cloud Engineering

Streamlining deployment pipelines and infrastructure for reliable, rapid delivery.

Tech Stack

The technologies and frameworks we leveraged to build a robust, scalable, and high-performance solution.

TensorFlow icon

TensorFlow

Deep Learning Framework

OpenCV icon

OpenCV

Computer Vision Library

React.js icon

React.js

Frontend Framework

Python icon

Python

Programming Language

PostgreSQL icon

PostgreSQL

Database System

AWS icon

AWS

Cloud Platform

Impact & Results
  • 1

    Enhanced Recognition Accuracy:

    Enhanced Recognition Accuracy: The system achieved a 95% accuracy rate in license plate detection across all weather conditions, a significant improvement from the previous system's 70% accuracy in suboptimal conditions.

  • 2

    Faster Incident Response:

    Faster Incident Response: Response times for traffic incidents improved by 30%, allowing for quicker resolution of issues and smoother traffic flow.

  • 3

    Cost Efficiency:

    Cost Efficiency: By integrating with the existing CCTV network, the project avoided an estimated $2 million in infrastructure upgrade costs.

  • 4

    Privacy and Compliance:

    Privacy and Compliance: The system passed all privacy audits with zero compliance issues, ensuring trust and security for both authorities and the public.

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