Security Alarm System Project Using Ultralytics YOLO11
The Security Alarm System Project utilizing Ultralytics YOLO11 integrates advanced computer vision capabilities to enhance security measures. YOLO11, developed by Ultralytics, provides real-time object detection, allowing the system to identify and respond to potential security threats promptly. This project offers several advantages:
- Real-time Detection: YOLO11's efficiency enables the Security Alarm System to detect and respond to security incidents in real-time, minimizing response time.
- Accuracy: YOLO11 is known for its accuracy in object detection, reducing false positives and enhancing the reliability of the security alarm system.
- Integration Capabilities: The project can be seamlessly integrated with existing security infrastructure, providing an upgraded layer of intelligent surveillance.
Watch: Security Alarm System Project with Ultralytics YOLO11 Object Detection
Code
Set up the parameters of the message
Note
App Password Generation is necessary
- Navigate to App Password Generator, designate an app name such as "security project," and obtain a 16-digit password. Copy this password and paste it into the designated password field as instructed.
password = ""
from_email = "" # must match the email used to generate the password
to_email = "" # receiver email
Server creation and authentication
import smtplib
server = smtplib.SMTP("smtp.gmail.com: 587")
server.starttls()
server.login(from_email, password)
Email Send Function
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
def send_email(to_email, from_email, object_detected=1):
"""Sends an email notification indicating the number of objects detected; defaults to 1 object."""
message = MIMEMultipart()
message["From"] = from_email
message["To"] = to_email
message["Subject"] = "Security Alert"
# Add in the message body
message_body = f"ALERT - {object_detected} objects has been detected!!"
message.attach(MIMEText(message_body, "plain"))
server.sendmail(from_email, to_email, message.as_string())
Object Detection and Alert Sender
from time import time
import cv2
import torch
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors
class ObjectDetection:
def __init__(self, capture_index):
"""Initializes an ObjectDetection instance with a given camera index."""
self.capture_index = capture_index
self.email_sent = False
# model information
self.model = YOLO("yolo11n.pt")
# visual information
self.annotator = None
self.start_time = 0
self.end_time = 0
# device information
self.device = "cuda" if torch.cuda.is_available() else "cpu"
def predict(self, im0):
"""Run prediction using a YOLO model for the input image `im0`."""
results = self.model(im0)
return results
def display_fps(self, im0):
"""Displays the FPS on an image `im0` by calculating and overlaying as white text on a black rectangle."""
self.end_time = time()
fps = 1 / round(self.end_time - self.start_time, 2)
text = f"FPS: {int(fps)}"
text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 1.0, 2)[0]
gap = 10
cv2.rectangle(
im0,
(20 - gap, 70 - text_size[1] - gap),
(20 + text_size[0] + gap, 70 + gap),
(255, 255, 255),
-1,
)
cv2.putText(im0, text, (20, 70), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 0), 2)
def plot_bboxes(self, results, im0):
"""Plots bounding boxes on an image given detection results; returns annotated image and class IDs."""
class_ids = []
self.annotator = Annotator(im0, 3, results[0].names)
boxes = results[0].boxes.xyxy.cpu()
clss = results[0].boxes.cls.cpu().tolist()
names = results[0].names
for box, cls in zip(boxes, clss):
class_ids.append(cls)
self.annotator.box_label(box, label=names[int(cls)], color=colors(int(cls), True))
return im0, class_ids
def __call__(self):
"""Run object detection on video frames from a camera stream, plotting and showing the results."""
cap = cv2.VideoCapture(self.capture_index)
assert cap.isOpened()
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
frame_count = 0
while True:
self.start_time = time()
ret, im0 = cap.read()
assert ret
results = self.predict(im0)
im0, class_ids = self.plot_bboxes(results, im0)
if len(class_ids) > 0: # Only send email If not sent before
if not self.email_sent:
send_email(to_email, from_email, len(class_ids))
self.email_sent = True
else:
self.email_sent = False
self.display_fps(im0)
cv2.imshow("YOLO11 Detection", im0)
frame_count += 1
if cv2.waitKey(5) & 0xFF == 27:
break
cap.release()
cv2.destroyAllWindows()
server.quit()
Call the Object Detection class and Run the Inference
That's it! When you execute the code, you'll receive a single notification on your email if any object is detected. The notification is sent immediately, not repeatedly. However, feel free to customize the code to suit your project requirements.
Email Received Sample
FAQ
How does Ultralytics YOLO11 improve the accuracy of a security alarm system?
Ultralytics YOLO11 enhances security alarm systems by delivering high-accuracy, real-time object detection. Its advanced algorithms significantly reduce false positives, ensuring that the system only responds to genuine threats. This increased reliability can be seamlessly integrated with existing security infrastructure, upgrading the overall surveillance quality.
Can I integrate Ultralytics YOLO11 with my existing security infrastructure?
Yes, Ultralytics YOLO11 can be seamlessly integrated with your existing security infrastructure. The system supports various modes and provides flexibility for customization, allowing you to enhance your existing setup with advanced object detection capabilities. For detailed instructions on integrating YOLO11 in your projects, visit the integration section.
What are the storage requirements for running Ultralytics YOLO11?
Running Ultralytics YOLO11 on a standard setup typically requires around 5GB of free disk space. This includes space for storing the YOLO11 model and any additional dependencies. For cloud-based solutions, Ultralytics HUB offers efficient project management and dataset handling, which can optimize storage needs. Learn more about the Pro Plan for enhanced features including extended storage.
What makes Ultralytics YOLO11 different from other object detection models like Faster R-CNN or SSD?
Ultralytics YOLO11 provides an edge over models like Faster R-CNN or SSD with its real-time detection capabilities and higher accuracy. Its unique architecture allows it to process images much faster without compromising on precision, making it ideal for time-sensitive applications like security alarm systems. For a comprehensive comparison of object detection models, you can explore our guide.
How can I reduce the frequency of false positives in my security system using Ultralytics YOLO11?
To reduce false positives, ensure your Ultralytics YOLO11 model is adequately trained with a diverse and well-annotated dataset. Fine-tuning hyperparameters and regularly updating the model with new data can significantly improve detection accuracy. Detailed hyperparameter tuning techniques can be found in our hyperparameter tuning guide.
