Smart Traffic Sign Detection
Brief
An intelligent traffic sign recognition system built with TensorFlow and OpenCV that accurately detects and classifies traffic signs in real-time from video feeds or still images, even in challenging visual conditions.
This project showcases advanced computer vision and deep learning techniques with a robust cloud deployment architecture. The system performs real-time traffic sign detection and classification with high accuracy, making it suitable for autonomous vehicle applications, driver assistance systems, and traffic monitoring.
My Contribution
As the lead developer and ML engineer, I was responsible for the entire machine learning pipeline and AWS cloud architecture:
- Developed scalable Python web applications using Django and AWS Lambda, optimizing image recognition pipelines for real-time traffic sign recognition. Achieved 95% accuracy processing 1,000+ images per second even in challenging lighting conditions.
- Implemented RESTful API services using AWS API Gateway, enabling seamless integration with client applications. This modular architecture reduced integration time by 40% for new clients and provided a consistent interface.
- Designed and trained custom convolutional neural networks optimized for traffic sign recognition, using data augmentation techniques to ensure model robustness across various environmental conditions.
- Architected a serverless cloud infrastructure that automatically scales with demand, minimizing operational costs while maintaining high availability and performance.
System Architecture
The system uses a serverless architecture on AWS to handle image processing at scale:
- Client Upload: Images uploaded through web or mobile interface
- API Gateway: RESTful endpoints receiving upload requests
- Lambda Processing: Serverless functions running TensorFlow models
- S3 Storage: Original and processed images stored for reference
- Django Backend: Web application for results visualization and management
Key Features
Real-time Processing
Optimized the image recognition pipeline to process images in real-time, achieving 95% accuracy while handling 1,000 images per second.
Cloud Integration
Leveraged AWS services including Lambda, S3, and API Gateway to create a scalable, cost-effective solution that automatically scales with demand.
Technical Challenges
During development, several challenges had to be overcome:
- Model Size Optimization: Reduced the TensorFlow model size by 60% without sacrificing accuracy to fit within Lambda's deployment constraints.
- Latency Reduction: Implemented caching and parallel processing to reduce response times by 35%.
- Environmental Variations: Trained the model on diverse datasets to ensure accuracy across different lighting conditions, weather, and environments.
Takeaways
This project provided valuable experience in building production-ready ML systems with a focus on performance and scalability. The techniques developed for model optimization and cloud deployment are applicable to a wide range of computer vision applications.