AI-Powered Real-Time Object Recognition & Detection System using YOLOv11, Flask, and React Native
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 (Expo), 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.
- Begin real-time detection through either the mobile camera or the webcam (via the Flask web interface).
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.
Screenshots
Flask Web Interface with Webcam
Mobile UI with Detection
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.
Contact Us
🚀 Codex Souls – AI & Mobile Development Experts
FAQ,s
YOLOv11 (You Only Look Once version 11) is the latest advancement in the YOLO family of object detection models. It builds upon the speed and accuracy of YOLOv5 and YOLOv7 while introducing improved architecture, better anchor box prediction, and support for advanced deployment formats like ONNX. YOLOv11 is particularly optimized for real-time use cases on both edge and mobile devices, making it ideal for live detection systems.
Our trained YOLOv11 model achieves over 90% accuracy in detecting and classifying road vehicles such as cars, trucks, buses, and long vehicles. This accuracy has been validated using a custom dataset, real-time webcam tests, and mobile camera environments. Additionally, the model maintains high precision and recall, ensuring reliable detection even in complex scenes or varying lighting conditions.
Yes. The YOLOv11 model is converted to the ONNX format and integrated with the ONNX Runtime, enabling the app to perform object detection on-device without needing internet access. The only online requirement is during initial setup or if using the optional Flask backend via ngrok.
The Flask backend can be hosted on any system with Python 3.7+ and basic hardware (4GB+ RAM recommended). It supports deployment on:
Local development machines (using XAMPP/localhost)
Cloud platforms (Heroku, AWS EC2, or DigitalOcean)
HTTPS tunneling using ngrok for secure mobile access during development or testing
Absolutely. The architecture is built to be modular and scalable. You can retrain the YOLOv11 model using your own dataset and labels (e.g., for pedestrians, animals, construction equipment, etc.). The detection logic, logs, and UI are designed to adapt to new object classes with minimal changes.
