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yolo11 custom object detection | yolo11 custom segmentation | yolo11 object tracking | yolov11

Science & Technology

Introduction

Welcome to the Freedom Tech YouTube channel! In this session, we will explore the exciting new capabilities of YOLOv11. We will learn how to use YOLOv11 for object detection, object tracking, vehicle speed estimation, instant segmentation, and even create our custom object detection and segmentation models. Let’s dive right in!

Introduction to YOLOv11

With YOLOv11 just around the corner, it provides advanced capabilities for real-time object detection and tracking. In this guide, we'll walk through the necessary steps for using YOLOv11 effectively, covering both object detection and segmentation with our custom models.

Setting Up Your Environment

To begin, we need to set up our YOLOv11 environment. We recommend using Python with the necessary libraries, including OpenCV, Ultralytics, and CV Zone. Once you’ve set up your environment, download the YOLOv11 repository and extract its contents.

Next, make sure to install the required packages:

  • OpenCV
  • Ultralytics (ensure it’s upgraded)
  • CV Zone

After installation, we can start writing code for object detection.

Object Detection with YOLOv11

  1. Opening the Code: Use your Python IDE (like Thonny) to open yolov11_object_detection_track.py.

  2. Model Selection: For demonstration purposes, we use the YOLOv11 Nano model (yolov11n.pt) due to hardware limitations. If you have a powerful GPU, you could opt for larger models like yolov11s.pt or yolov11l.pt.

  3. Real-Time Detection: We employ the model.track method from the Ultralytics package. The following code snippet initializes the camera, captures frames, and detects objects:

    cap = cv2.VideoCapture(0)
results = model.track(frame)

  4. Processing Results: We loop through the results to extract bounding boxes, class IDs, and track IDs. From there, we draw rectangles and labels on detected objects.

  5. Running the Code: Execute the code to begin detecting objects in real time. The system will label and track detected objects, including the ability to differentiate between them.

Object Tracking and Vehicle Speed Estimation

In addition to simple detection, we can monitor vehicle speed using the bounding box center points. By drawing a line in the frame and detecting when an object crosses it, we can estimate its speed.

Instant Segmentation

Next, we’ll show how to create an instant segmentation model with YOLOv11. This involves using a similar approach to the object detection process but involves masks for each detected object:

  1. Loading the YOLOv11 Segmentation Model: Use yolov11n_hy.pt for instant segmentation.

  2. Modifying the Code: Implement the necessary code to capture frames and apply the segmentation masks accordingly.

  3. Visualization: Display segmented outputs on the frame, illustrating not only the bounding boxes but also the pixels related to detected classes.

Creating Custom Object Detection Models

Now, let’s delve into creating custom models. We will detect three custom objects: Arduino Uno, ESP32, and a motion sensor.

  1. Capturing Images: Using a USB web camera, you will capture images for each class.

  2. Annotation: Utilize a labeling tool like LabelImg to annotate the images captured. Save the annotation files to facilitate training.

  3. Training Your Model: Upload the images and labels to Google Drive and execute training scripts in Google Colab.

  4. Model Evaluation: Download the trained model and run the detection code to test it with your custom objects.

Custom Segmentation Model with Roboflow

To create a custom segmentation model, follow steps similar to object detection:

  1. Annotation in Roboflow: Upload your images to Roboflow, annotate them using smart polygons for each detected class.

  2. Training: Follow the instructions to generate a dataset in the latest YOLO format, then train your segmentation model.

  3. Final Evaluation: Test the trained segmentation model and evaluate its performance on the dataset.

Conclusion

By following these steps, you can effectively use YOLOv11 for various applications, including custom object detection, segmentation, and tracking. This guide has equipped you with the knowledge to harness YOLOv11's capabilities for your projects.

Keyword

YOLOv11, Custom Object Detection, Custom Segmentation, Object Tracking, Vehicle Speed Estimation, Real-Time Detection, Python, OpenCV, Roboflow, Ultralytics.

FAQ

Q: What is YOLOv11?
A: YOLOv11 is a state-of-the-art object detection model that provides advanced capabilities for real-time applications, including tracking and segmentation.

Q: How do you set up the YOLOv11 environment?
A: To set up the environment, you need Python with libraries like OpenCV, Ultralytics, and CV Zone. You can download the YOLOv11 repository and install the required packages.

Q: Can I create custom object detection models using YOLOv11?
A: Yes, you can create custom models by capturing images of your target objects, annotating them, and then training the model using the YOLO framework.

Q: What applications can be built with YOLOv11?
A: Applications include real-time object detection, vehicle speed estimation, and instant segmentation for various use cases in safety and automation.

Q: Is it possible to perform instant segmentation with YOLOv11?
A: Yes, YOLOv11 supports instant segmentation, allowing users to detect and segment objects in real-time effectively.