https://github.com/ginnters/realtime-object-detection-warning

Real-time YOLO-based object detection system with danger zone alerts, Telegram and Email notifications for safety automation.
https://github.com/ginnters/realtime-object-detection-warning

computer-vision detection email-notification object-detection opencv safety telegram-api telegram-api-bot yolo

Last synced: 3 months ago
JSON representation

Real-time YOLO-based object detection system with danger zone alerts, Telegram and Email notifications for safety automation.

• Host: GitHub
• URL: https://github.com/ginnters/realtime-object-detection-warning
• Owner: GinnTers
• Created: 2025-06-26T04:28:50.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2025-06-29T00:31:21.000Z (7 months ago)
• Last Synced: 2025-06-29T01:36:15.763Z (7 months ago)
• Topics: computer-vision, detection, email-notification, object-detection, opencv, safety, telegram-api, telegram-api-bot, yolo
• Language: Python
• Homepage:
• Size: 58.6 MB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Real-Time Object Detection Warning System  

> Project conducted: May 2024 (was not uploaded at the time)

A real-time computer vision system using YOLO for object detection with danger zone alerts, sound notifications, and automated Telegram & Email warnings.

---

## Introduction

![Eva](assets/1.jpg)

This project aims to build an automated **safety warning system** that uses computer vision and object detection to identify intruding objects within a predefined danger zone. When a detected object enters the danger area, the system will:

- Play an alarm sound locally  

- Send a warning message with image evidence via **Telegram** and **Email**

**Key Applications:**

- Intruder detection for security systems  

- Safety monitoring in industrial zones  

- Automated alerts in self-driving car assistance systems

---

## Dataset

### Source

- **Images:** 5,074 annotated images (4712 person, 362 non-person)

- **Model:** Pre-trained YOLOv4 model used for object detection

### Performance

- **Precision:** 0.9985  

- **Recall:** 0.9985

![Eva](assets/eva.jpg)

---

## Tools & Technologies

- **Languages:** Python  

- **Libraries:** OpenCV, YOLOv4, Shapely, smtplib, telegram API  

- **Hardware:** Requires GPU for optimal real-time performance

---

## Project Workflow

### 1. Setup & Running the System

- Run `code/main.py`  

- The script will:

  - Open webcam feed  

  - Allow the user to define a danger zone by clicking 4 points using the mouse (`D` key to finalize)  

  - Start real-time object detection using YOLOv4

  - Continuously check if any detected object’s coordinates fall inside the defined danger zone

### 2. Object Detection

- Utilizes **YOLOv4** to detect objects within the camera feed in real-time.

- Bounding boxes are drawn around detected objects with labels and confidence scores.

![Eva](assets/detect.webp)

### 3. Danger Zone Checking

- Uses **Shapely** library to:

  - Define and process the user-drawn polygon as the danger zone

  - Check if object centroids fall within this zone

![Eva](assets/2.jpg)

### 4. Warning & Notification

- If an object enters the danger zone:

  - **Sound alarm:** plays a local audio file as an immediate alert  

  - **Telegram alert:** sends a photo with a warning message to a pre-configured chat  

  - **Email notification:** sends an email with the captured frame and warning details

---

## Result

Running the system displays:

- Real-time webcam window with bounding boxes and danger zone overlay  

- Console log of detection events and notification status  

- Sends alerts to configured Telegram chat and Email address when an intrusion is detected

---

## How to Use

1. Clone the repository and install required libraries (`code/setup.txt`).  

2. Configure your Telegram bot token, chat ID, and email SMTP settings in ``code/main.py``.  

3. Run:

   ```bash

   python main.py

## Project Resources

To understand this project in detail:

- Presentation: Read the project presentation slides in `slide_presentation.pdf`

- Demo Video: Watch the [demo video](https://www.youtube.com/watch?v=orFa52maBXw) to see the system in action

**Both files are included in this repository for better understanding of the workflow, functionality, and real-world application of the system.**

## Learning Outcomes

- Built a functional real-time object detection system with YOLOv4.

- Integrated computer vision with notification APIs for automated alerts.

- Applied spatial geometry checking (Shapely) for zone-based detection.

- Strengthened skills in Python, OpenCV, and deployment of AI-powered safety solutions.

## Requirements

- opencv-python

- numpy

- imutils

- python-telegram-bot

**Please install using pip and ensure compatible versions based on your Python environment.**