AI-Powered Real-Time Object Detection App
Table of Contents
Overview
In the field of AI and computer vision, real-time object detection is transforming industries. I built a robust AI-powered object recognition system using YOLOv11, integrated with Flask and React Native, optimized for mobile and web interfaces. The system is trained to detect vehicles like cars, buses, trucks, and long vehicles with real-time feedback and strong performance.
This solution supports both mobile detection using React Native and webcam-based live detection via a Flask web interface.
🔗 GitHub Repositories:
Key Features
Live camera feed for instant detection
Flask-powered web interface with webcam integration
ONNX-optimized YOLOv11 model for efficient mobile and desktop inference
Secure HTTPS communication using ngrok
Detection logs and real-time object tracking
React Native UI for Android mobile access
Architecture & Tech Stack
| Layer | Technology Used |
|---|---|
| Frontend | React Native, Flask Web UI |
| Backend | Python, Flask REST API |
| AI Model | YOLOv11 → ONNX, Ultralytics |
| Deployment | ONNX Runtime, ngrok HTTPS tunnels |
| Other Tools | OpenCV, CV2, Albumentations, Real-time Logging |
Workflow
Start Flask API server locally or on a cloud service.
- This system includes the Flask web frontend and backend along with the full YOLOv11 model training code.
Model Performance & Accuracy
Our YOLOv11 model was trained on a custom dataset focused on road vehicles. Using advanced data augmentation techniques and extensive tuning, we achieved:
Over 90% accuracy in classifying and detecting vehicles such as cars, buses, trucks, and long vehicles.
High precision-recall balance, ensuring minimal false positives.
Optimized inference speed using ONNX Runtime, making it suitable for real-time use on mobile devices.
Model evaluation was conducted using both validation datasets and real-time user tests to ensure reliability in live environments.
Impact
Enabled real-time recognition of multiple object types with high accuracy.
Designed to work smoothly in low-connectivity or remote regions.
Built with scalability in mind — adaptable for new classes like animals, pedestrians, or industrial machinery.
Client Use Cases
Smart Traffic Systems — Real-time vehicle flow analysis and violation detection.
Surveillance — Detect and record suspicious objects or vehicle types.
Fleet Management — Track and analyze fleet behavior in real time.
Urban Planning — Collect data for road usage and congestion management.
Keywords (SEO Optimized)
Short Tail Keywords:
yolo meaning, real-time detection, flask web app, object detection, React Native AI app
Long Tail Keywords:
yolo object detection with React Native, flask yolo api integration, YOLOv11 vs YOLOv5 model performance, flask webcam object detection tutorial, on-device object recognition, YOLOv11 ONNX model mobile app
Conclusion
This system bridges cutting-edge AI detection with real-world use cases through a seamless mobile and web-based experience. By leveraging YOLOv11’s high accuracy and React Native’s mobile flexibility, this project offers a scalable, secure, and powerful solution for real-time object detection.
Whether you’re working on smart city solutions, security applications, or data analysis for road environments, this technology is production-ready and designed for impact.
