Privacy Protection
==================

During the exploration of data protection policies, I utilized the latest release from Ultralytics: **YOLOv11**. Compared to YOLOv8, YOLOv11 is significantly faster and more accurate, making it an excellent choice for implementing privacy features like object blurring.

Benefits of Using YOLOv11 for Object Blurring
---------------------------------------------

1. **Privacy Protection:**  
   Effectively obscures sensitive or identifiable information, ensuring compliance with data protection policies like GDPR.

2. **Selective Focus:**  
   Targets specific objects (e.g., faces) for blurring, while maintaining essential visual content for navigation and safety.

3. **Real-Time Processing:**  
   Executes object blurring efficiently in dynamic environments, making it suitable for instant privacy enhancements.

Using YOLOv11, I trained and deployed a model on the **Go2 robot**. As shown in the **GIF below**, the robot can detect a person’s face, blur the region effectively, and display this blurred view on its screen. This functionality works as long as the person remains within the robot camera’s frame.

.. figure:: media/gifs/faceblur.webp                                                                
   :width: 100%                                                                                                                         
   :align: center  
   :class: mbsrounded 
                                    
   *Facedetection and Faceblurring* 

YOLOv11 Performance Comparison
------------------------------

The chart below illustrates how YOLOv11 outperforms earlier models like YOLOv8 in terms of speed and accuracy. YOLOv11 introduces significant improvements, making it the preferred choice for real-time applications such as privacy-focused object detection and blurring.

.. image:: https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png
   :alt: YOLOv11 Performance Comparison
   :target: https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png

`Ultralytics Performance Reference <https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png>`_

Key Observations
----------------

- **Speed:**  
  YOLOv11 processes frames faster than YOLOv8, making it ideal for dynamic environments like retail stores where robots must respond quickly.

- **Accuracy:**  
  YOLOv11 demonstrates higher detection accuracy compared to YOLOv8, ensuring reliable identification and processing of objects and faces.

- **Efficiency in Privacy Tasks:**  
  With improved performance metrics, YOLOv11 ensures privacy protection features, such as object blurring, are executed without compromising system speed or functionality.

Using YOLOv11, I trained and deployed a model on the **Go2 robot**. The robot effectively detects a person’s face, blurs the region, and displays this securely on its screen while ensuring smooth operation.

Generalization Across Cameras
-----------------------------

.. note::
    While this application was tested with the Go2 robot, it is designed to work with other cameras as well, including the **ZED 2i**. This flexibility ensures the solution can be applied across different hardware configurations, enhancing its usability in diverse environments.
    For more details about YOLOv11, refer to the `Ultralytics YOLO11 Documentation <https://docs.ultralytics.com/models/yolo11/>`_.