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Update YOLO11 Actions and Docs (#16596)

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docs/en/guides/security-alarm-system.md
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---
comments: true
description: Enhance your security with real-time object detection using Ultralytics YOLOv8. Reduce false positives and integrate seamlessly with existing systems.
keywords: YOLOv8, Security Alarm System, real-time object detection, Ultralytics, computer vision, integration, false positives
description: Enhance your security with real-time object detection using Ultralytics YOLO11. Reduce false positives and integrate seamlessly with existing systems.
keywords: YOLO11, Security Alarm System, real-time object detection, Ultralytics, computer vision, integration, false positives
---
# Security Alarm System Project Using Ultralytics YOLOv8
# Security Alarm System Project Using Ultralytics YOLO11
<img src="https://github.com/ultralytics/docs/releases/download/0/security-alarm-system-ultralytics-yolov8.avif" alt="Security Alarm System">
The Security Alarm System Project utilizing Ultralytics YOLOv8 integrates advanced [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) capabilities to enhance security measures. YOLOv8, 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:
The Security Alarm System Project utilizing Ultralytics YOLO11 integrates advanced [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) 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:** YOLOv8's efficiency enables the Security Alarm System to detect and respond to security incidents in real-time, minimizing response time.
- **[Accuracy](https://www.ultralytics.com/glossary/accuracy):** YOLOv8 is known for its accuracy in object detection, reducing false positives and enhancing the reliability of the security alarm system.
- **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](https://www.ultralytics.com/glossary/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.
<p align="center">
@ -22,7 +22,7 @@ The Security Alarm System Project utilizing Ultralytics YOLOv8 integrates advanc
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Security Alarm System Project with Ultralytics YOLOv8 <a href="https://www.ultralytics.com/glossary/object-detection">Object Detection</a>
<strong>Watch:</strong> Security Alarm System Project with Ultralytics YOLO11 <a href="https://www.ultralytics.com/glossary/object-detection">Object Detection</a>
</p>
### Code
@ -90,7 +90,7 @@ class ObjectDetection:
self.email_sent = False
# model information
self.model = YOLO("yolov8n.pt")
self.model = YOLO("yolo11n.pt")
# visual information
self.annotator = None
@ -155,7 +155,7 @@ class ObjectDetection:
self.email_sent = False
self.display_fps(im0)
cv2.imshow("YOLOv8 Detection", im0)
cv2.imshow("YOLO11 Detection", im0)
frame_count += 1
if cv2.waitKey(5) & 0xFF == 27:
break
@ -179,22 +179,22 @@ That's it! When you execute the code, you'll receive a single notification on yo
## FAQ
### How does Ultralytics YOLOv8 improve the accuracy of a security alarm system?
### How does Ultralytics YOLO11 improve the accuracy of a security alarm system?
Ultralytics YOLOv8 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.
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 YOLOv8 with my existing security infrastructure?
### Can I integrate Ultralytics YOLO11 with my existing security infrastructure?
Yes, Ultralytics YOLOv8 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 YOLOv8 in your projects, visit the [integration section](https://docs.ultralytics.com/integrations/).
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](https://docs.ultralytics.com/integrations/).
### What are the storage requirements for running Ultralytics YOLOv8?
### What are the storage requirements for running Ultralytics YOLO11?
Running Ultralytics YOLOv8 on a standard setup typically requires around 5GB of free disk space. This includes space for storing the YOLOv8 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](../hub/pro.md) for enhanced features including extended storage.
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](../hub/pro.md) for enhanced features including extended storage.
### What makes Ultralytics YOLOv8 different from other object detection models like Faster R-CNN or SSD?
### What makes Ultralytics YOLO11 different from other object detection models like Faster R-CNN or SSD?
Ultralytics YOLOv8 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](https://www.ultralytics.com/glossary/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](https://docs.ultralytics.com/models/).
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](https://www.ultralytics.com/glossary/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](https://docs.ultralytics.com/models/).
### How can I reduce the frequency of false positives in my security system using Ultralytics YOLOv8?
### How can I reduce the frequency of false positives in my security system using Ultralytics YOLO11?
To reduce false positives, ensure your Ultralytics YOLOv8 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](https://www.ultralytics.com/glossary/hyperparameter-tuning) techniques can be found in our [hyperparameter tuning guide](../guides/hyperparameter-tuning.md).
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](https://www.ultralytics.com/glossary/hyperparameter-tuning) techniques can be found in our [hyperparameter tuning guide](../guides/hyperparameter-tuning.md).