https://github.com/burhanali2211/object_detection_for_home_security
This project implements real-time AI-based object detection using YOLOv8 for home security
https://github.com/burhanali2211/object_detection_for_home_security
ai home-security python yolov8
Last synced: 3 months ago
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This project implements real-time AI-based object detection using YOLOv8 for home security
- Host: GitHub
- URL: https://github.com/burhanali2211/object_detection_for_home_security
- Owner: Burhanali2211
- Created: 2025-02-12T17:31:14.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-12T17:37:35.000Z (4 months ago)
- Last Synced: 2025-02-12T18:36:42.407Z (4 months ago)
- Topics: ai, home-security, python, yolov8
- Language: Python
- Homepage:
- Size: 19.5 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# AI-Based Object Detection for Home Security
## Overview
This project implements real-time **AI-based object detection** using **YOLOv8** for home security. It detects objects, humans, and animals using a webcam and provides fast and accurate results.
## Features
✅ **Real-time object detection** using YOLOv8.
✅ **Optimized for speed** with NumPy and multi-threading.
✅ **Accurate predictions** using `yolov8s.pt`.
✅ **Displays detected objects** with confidence scores.
✅ **Runs smoothly on mid-range laptops & PCs**.
## Requirements
Make sure you have **Python 3.8+** installed, then install dependencies:
```bash
pip install opencv-python torch torchvision torchaudio ultralytics numpy
```
## Usage
Run the following command to start the object detection system:
```bash
python Object_Detection_for_Home_Security.py
```
## How It Works
1. Loads the **YOLOv8-Small (`yolov8s.pt`)** model.
2. Captures video from the **webcam**.
3. Uses **NumPy** for fast frame processing.
4. Runs YOLOv8 to **detect objects in real-time**.
5. Draws **bounding boxes & labels** for detected objects.
6. Press **'q'** to exit the detection system.
## Code
```python
import cv2
import torch
import numpy as np
import threading
from ultralytics import YOLO
# Load YOLOv8 Small model (better accuracy than Nano)
model = YOLO("yolov8s.pt")
# Open webcam
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("Error: Couldn't access the webcam.")
exit()
# Function to process frame
def process_frame():
global frame
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Convert frame to NumPy array for fast processing
frame_np = np.array(frame, dtype=np.uint8)
# Run YOLOv8 on frame
results = model(frame_np, verbose=False) # Disable logs for speed
# Extract detected objects
for result in results:
for box in result.boxes:
x1, y1, x2, y2 = map(int, box.xyxy[0]) # Bounding box
conf = round(box.conf[0].item(), 2) # Confidence score
cls = int(box.cls[0].item()) # Class index
label = f"{model.names[cls]} {conf:.2f}"
# Draw bounding box & label
cv2.rectangle(frame_np, (x1, y1), (x2, y2), (0, 255, 0), 2, lineType=cv2.LINE_AA)
cv2.putText(frame_np, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2, lineType=cv2.LINE_AA)
# Show frame
cv2.imshow("AI Object Detection", frame_np)
# Exit on 'q'
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Run detection in a separate thread for better performance
thread = threading.Thread(target=process_frame)
thread.start()
thread.join()
cap.release()
cv2.destroyAllWindows()
```
## Notes
- If accuracy is not enough, you can replace `yolov8s.pt` with `yolov8m.pt` for a better model.
- If running on a **low-end PC**, use `yolov8n.pt` for a lighter version.
## Future Improvements
🚀 Add **motion detection** to trigger alerts.
🚀 Implement **email or SMS notifications** for security alerts.
🚀 Save **detected objects with timestamps** for logging purposes.
## License
This project is open-source under the **MIT License**.