🎯 DetectAI V2

Real-Time Object Detection & Analytics Platform

🚀 Live Demo · 📖 Docs · 🐛 Report Bug

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📌 Overview

DetectAI V2 is a production-ready computer vision web app built with YOLOv8, Streamlit, and OpenCV. It detects objects in images and live webcam feeds in real time, with a full analytics dashboard and export options — deployable to the cloud in minutes.

Built as a major upgrade from a YOLOv4 + Tkinter desktop project:

	v1 (Old)	v2 (This project)
Model	YOLOv4	YOLOv8 (5× faster, higher mAP)
UI	Tkinter (desktop)	Streamlit (web, cloud-ready)
Export	❌ None	✅ CSV, YOLO .txt, annotated JPEG
Analytics	❌ None	✅ Full dashboard
Deployment	Local only	✅ Render / Railway / Streamlit Cloud

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✨ Features

🖼️ Image Detection

• Upload single or multiple images (JPG, PNG, WEBP, BMP)
• YOLOv8 bounding boxes with confidence scores
• Previous / Next navigation for batch uploads
• Export as CSV, YOLO .txt annotations, or annotated JPEG

🎥 Live Camera Detection

• Real-time webcam object detection
• FPS overlay and live object counter
• Adjustable frame rate control
• Detection history saved per session

📊 Analytics Dashboard

• Top detected classes — bar chart
• Confidence distribution histogram
• Per-run history table
• Export all session data as CSV

⚙️ Model Configuration

• Switch between yolov8n / s / m / l / x with one click
• Tune confidence threshold and IoU / NMS threshold live
• Full COCO 80-class reference table

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🛠️ Tech Stack

Layer	Technology
Detection model	YOLOv8 (Ultralytics)
Web framework	Streamlit
Computer vision	OpenCV
Data processing	Pandas, NumPy
Deployment	Render / Railway / Streamlit Cloud

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📂 Project Structure

DetectAI-V2/
│
├── app.py                        # Entry point — sidebar navigation
├── requirements.txt              # All dependencies
├── render.yaml                   # One-click Render deployment config
├── README.md
│
├── pages/
│   ├── image_detection.py        # Upload & detect page
│   ├── live_camera.py            # Webcam real-time detection
│   ├── analytics.py              # Session analytics dashboard
│   └── model_config.py          # Model & threshold settings
│
├── utils/
│   ├── detector.py               # YOLOv8 inference engine + exporters
│   ├── session.py                # Session state & history manager
│   └── styles.py                 # Custom CSS injection
│
└── exports/                      # Auto-created — downloaded files land here

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🚀 Installation

Prerequisites

• Python 3.10 or higher
• Webcam (optional — only needed for Live Camera page)

Steps

# 1. Clone the repository
git clone https://github.com/geeta3521/DetectAI-V2.git
cd DetectAI-V2

# 2. Create and activate a virtual environment (recommended)
python -m venv venv
source venv/bin/activate       # Linux / Mac
venv\Scripts\activate          # Windows

# 3. Install dependencies
pip install -r requirements.txt

# 4. Run the app
streamlit run app.py

Open http://localhost:8501 in your browser.

Note: YOLOv8 weights (yolov8s.pt, ~22 MB) download automatically on first run.

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☁️ Deployment (Free)

Streamlit Community Cloud (easiest — recommended)

1. Push this repo to GitHub
2. Go to share.streamlit.io → Deploy an app
3. Select your repo and point to app.py
4. Done — you get a free yourapp.streamlit.app URL with HTTPS

Render

1. Push to GitHub
2. New Web Service → connect repo
3. Build command: pip install -r requirements.txt
4. Start command: streamlit run app.py --server.port $PORT --server.address 0.0.0.0 --server.headless true
5. render.yaml in this repo does this automatically

Railway

Same as Render — Railway auto-detects Streamlit projects.

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🎯 Supported Classes (80 COCO)

Click to expand full class list

person · bicycle · car · motorcycle · airplane · bus · train · truck · boat · traffic light · fire hydrant · stop sign · parking meter · bench · bird · cat · dog · horse · sheep · cow · elephant · bear · zebra · giraffe · backpack · umbrella · handbag · tie · suitcase · frisbee · skis · snowboard · sports ball · kite · baseball bat · baseball glove · skateboard · surfboard · tennis racket · bottle · wine glass · cup · fork · knife · spoon · bowl · banana · apple · sandwich · orange · broccoli · carrot · hot dog · pizza · donut · cake · chair · couch · potted plant · bed · dining table · toilet · tv · laptop · mouse · remote · keyboard · cell phone · microwave · oven · toaster · sink · refrigerator · book · clock · vase · scissors · teddy bear · hair drier · toothbrush

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📈 Roadmap

• YOLOv8 image detection with bounding boxes
• Live webcam detection with FPS monitoring
• Analytics dashboard with class distribution
• CSV / YOLO annotation export
• Multi-model switching (n/s/m/l/x)
• Video file upload detection
• Object tracking (DeepSORT / ByteTrack)
• Custom model training UI
• Multi-camera support
• AI-generated detection report (PDF)

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👩‍💻 Author

Geeta A N BE — Artificial Intelligence & Machine Learning

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📄 License

This project is licensed under the MIT License — see LICENSE for details.

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