Object Detection with YOLO Algorithm Activity

What you'll learn

• How to install and configure the Ultralytics YOLO package
• Understanding YOLO model class names and detection capabilities
• Implementing real-time object detection on images
• Building object detection for video files
• Creating webcam-based detection applications
• Developing advanced object counting systems

Activity Overview

Total Time: 90-120 minutes

• Setup & Installation: 15 minutes
• Basic Detection: 60 minutes
• Advanced Challenge: 30-45 minutes

Activity Type: 💻 Coding & Implementation

Read, Think, Code

Question One: Setup and Installation ⏱️ 15 minutes

Objective: Install the YOLO package and verify installation

Steps to complete:

1. Open your terminal or command prompt
2. Navigate to your project directory
3. Run the appropriate installation command for your operating system:

• Windows:

py -m pip install -r requirements.txt

• MacOS:

python3 -m pip install -r requirements.txt

💡 Installation Success If successful, you should see a version number printed in your terminal (may vary depending on install date). If you encounter errors, make sure you have Python 3.7+ installed and pip is up to date.

Question 2: Understanding YOLO Model Classes ⏱️ 10 minutes

Objective: Explore what objects YOLO can detect

Steps to complete:

1. Create a new Python file called yolo_classes.py
2. Copy and run the following code:

from ultralytics import YOLO

# Load the trained model
model = YOLO('yolov8n.pt')

# Get class names
class_names = model.names

# Print class names
print(class_names)

# Save class names to file
with open('class_names.txt', 'w') as f:
    for index in class_names:
        f.write(f'{index}: {class_names[index]}\n')

3. Open the generated class_names.txt file to see all 80 object classes
4. Review the classes to understand what objects YOLO can detect

ℹ️ About YOLO Classes YOLOv8 can identify 80 different object classes including people, animals, vehicles, and everyday objects. Learn more about the dataset at the YOLOv8 documentation.

Question 3: Detecting Objects in Images ⏱️ 20 minutes

Objective: Implement object detection on a static image

Steps to complete:

1. Place an image file named outing.jpg in your project folder
2. Create a new file called image_detection.py
3. Copy and run the following code:

from ultralytics import YOLO
import cv2
import math

# Load model
model = YOLO('yolov8n.pt')
class_name = model.names

# Load image
img = cv2.imread('outing.jpg')

# Detect objects
results = model(img)

# Draw detections
for r in results:
    for box in r.boxes:
        # Bounding box
        x1, y1, x2, y2 = box.xyxy[0]
        cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 255), 3)
        
        # Confidence and class name
        conf = math.ceil((box.conf[0]*100))/100
        cls = int(box.cls[0])
        
        # Label
        label = f'{class_name[cls]}{conf}'
        cv2.putText(img, label, (int(x1), int(y1)-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)

# Display image
cv2.imshow('Image', img)
cv2.waitKey(0)

4. Press any key to close the detection window

💡 Common Issues

• No detections? Try using an image with clearly visible objects like people, cars, or animals
• Low confidence scores? This is normal for small or partially visible objects
• Error loading image? Check that outing.jpg is in the same folder as your Python file

Question 4: Real-time Video Detection ⏱️ 20 minutes

Objective: Apply YOLO detection to video files

Steps to complete:

1. Place a video file named video.mp4 in your project folder
2. Create a new file called video_detection.py
3. Copy and run the following code:

from ultralytics import YOLO
import cv2
import math

# Load model
model = YOLO('yolov8n.pt')
class_name = model.names

# Load video
video = cv2.VideoCapture('video.mp4')

while True:
    ret, frame = video.read()
    if not ret:
        break
    
    # Detect objects
    results = model(frame)
    
    # Draw detections
    for r in results:
        for box in r.boxes:
            x1, y1, x2, y2 = box.xyxy[0]
            cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 255), 2)
            
            conf = math.ceil((box.conf[0]*100))/100
            cls = int(box.cls[0])
            label = f'{class_name[cls]}{conf}'
            cv2.putText(frame, label, (int(x1), int(y1)-10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
    
    cv2.imshow('Video', frame)
    if cv2.waitKey(1) == ord('q'):
        break

video.release()
cv2.destroyAllWindows()

4. Press 'q' to stop the video detection

💡 Performance Tips

• For faster processing, use smaller video files or lower resolution
• If the video runs slowly, try using yolov8s.pt instead of yolov8n.pt
• The first run may be slower as the model loads

Question 5: Live Webcam Detection ⏱️ 15 minutes

Objective: Create a real-time object detection system using your webcam

Steps to complete:

1. Ensure your webcam is connected and working
2. Create a new file called webcam_detection.py
3. Copy and run the following code:

from ultralytics import YOLO
import cv2
import math

# Load model
model = YOLO('yolov8n.pt')
class_name = model.names

# Initialize webcam
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    
    # Detect objects
    results = model(frame)
    
    # Draw detections
    for r in results:
        for box in r.boxes:
            x1, y1, x2, y2 = box.xyxy[0]
            cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 255), 2)
            
            conf = math.ceil((box.conf[0]*100))/100
            cls = int(box.cls[0])
            label = f'{class_name[cls]}{conf}'
            cv2.putText(frame, label, (int(x1), int(y1)-10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
    
    cv2.imshow('Webcam', frame)
    if cv2.waitKey(1) == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

4. Press 'q' to stop the webcam detection

⚠️ Privacy Notice This application uses your webcam. Make sure you're in a suitable environment before running this code. The detection runs locally on your computer - no data is sent anywhere.

Advanced Level ⏱️ 30-45 minutes

Advanced Challenge One: Building an Object Counter 🎯

Objective: Create a specialized counter for specific object types

Challenge Requirements:

• Count the number of people in an image
• Count the number of cats in an image
• Display the results clearly

Steps to complete:

1. Find or take a photo with both people and cats
2. Save it as advanced_image.jpg
3. Create advanced_counter.py with the following code:

# Advanced-1.py
from ultralytics import YOLO
import cv2

model = YOLO('yolov8n.pt')
img = cv2.imread('advanced_image.jpg')

# Class IDs for person and cat
person_id = list(model.names.keys())[list(model.names.values()).index('person')]
cat_id = list(model.names.keys())[list(model.names.values()).index('cat')]

results = model(img)

person_count = 0
cat_count = 0

for r in results:
    for box in r.boxes:
        cls = int(box.cls[0])
        if cls == person_id:
            person_count += 1
        elif cls == cat_id:
            cat_count += 1

print(f"People: {person_count}, Cats: {cat_count}")

💡 Extension Ideas

• Add more object types to count (dogs, cars, etc.)
• Display the counts on the image itself
• Create a GUI interface for selecting which objects to count
• Save detection results to a CSV file

Troubleshooting Guide

Common Issues and Solutions

1. Installation Errors

   • Update pip: pip install --upgrade pip
   • Try installing in a virtual environment
   • Check Python version (needs 3.7+)

2. Model Download Issues

   • The first run downloads the model (~6MB)
   • Ensure stable internet connection
   • If download fails, try again

3. Detection Not Working

   • Check image/video file paths
   • Verify webcam permissions
   • Try with different test images

4. Performance Problems

   • Use smaller model: yolov8n.pt (nano) is fastest
   • Reduce image/video resolution
   • Close other applications

Exercise Submission

⚠️ Important: Submit Your Work! Submission Deadline: End of class

What to submit:

1. All your Python files (.py)
2. Screenshot of your detection results
3. Any modified code for the advanced challenge

Submit through this form:
📤 Exercise Submission Form

ℹ️ Need Help? If you're stuck:

1. Review the error messages carefully
2. Check the troubleshooting guide above
3. Ask your instructor or classmates
4. Consult the YOLO documentation