Object Detection (YOLO, R-CNN)

Find and label objects in images.

What is Object Detection?

Find where objects are and what they are.

Output: Bounding boxes + class labels!

YOLO (You Only Look Once)

Fast, real-time object detection.

# Install
# pip install ultralytics

from ultralytics import YOLO
import cv2

# Load pre-trained model
model = YOLO('yolov8n.pt')  # nano model (fastest)

# Detect objects in image
results = model('image.jpg')

# Show results
for result in results:
    boxes = result.boxes
    for box in boxes:
        # Get coordinates
        x1, y1, x2, y2 = box.xyxy[0]
        confidence = box.conf[0]
        class_id = box.cls[0]
        class_name = model.names[int(class_id)]
        
        print(f"{class_name}: {confidence:.2f}")
        print(f"Box: ({x1:.0f}, {y1:.0f}) to ({x2:.0f}, {y2:.0f})")

# Display results
result.show()

Real-time Video Detection

import cv2

# Open webcam
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break
    
    # Detect objects
    results = model(frame)
    
    # Draw boxes
    annotated_frame = results[0].plot()
    
    # Display
    cv2.imshow('YOLO Detection', annotated_frame)
    
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Custom Object Detection

# Train on custom dataset
model = YOLO('yolov8n.pt')

# Train
results = model.train(
    data='custom_data.yaml',  # Dataset config
    epochs=100,
    imgsz=640,
    batch=16,
    device=0  # GPU
)

# Validate
metrics = model.val()

# Use trained model
model = YOLO('runs/detect/train/weights/best.pt')
results = model('test_image.jpg')

Dataset Format (YOLO)

Create custom_data.yaml file:

train: /path/to/train/images
val: /path/to/val/images

nc: 3  # number of classes
names: ['person', 'car', 'bicycle']

Label format (one .txt file per image):

# class_id center_x center_y width height (normalized 0-1)
0 0.5 0.5 0.3 0.4
1 0.7 0.3 0.2 0.3

Faster R-CNN

More accurate but slower:

import torchvision
from torchvision.models.detection import fasterrcnn_resnet50_fpn

# Load model
model = fasterrcnn_resnet50_fpn(pretrained=True)
model.eval()

# Prepare image
import torch
from PIL import Image
from torchvision import transforms

image = Image.open('image.jpg')
transform = transforms.Compose([transforms.ToTensor()])
image_tensor = transform(image).unsqueeze(0)

# Detect
with torch.no_grad():
    predictions = model(image_tensor)

# Process results
boxes = predictions[0]['boxes'].cpu().numpy()
labels = predictions[0]['labels'].cpu().numpy()
scores = predictions[0]['scores'].cpu().numpy()

for box, label, score in zip(boxes, labels, scores):
    if score > 0.5:  # Confidence threshold
        print(f"Label: {label}, Score: {score:.2f}")
        print(f"Box: {box}")

Comparison

YOLO:

• Very fast (real-time)
• Good for video
• Slightly less accurate

Faster R-CNN:

• More accurate
• Slower
• Better for images

EfficientDet:

• Balance of speed and accuracy
• Efficient architecture

Applications

• Self-driving cars
• Security cameras
• Retail analytics
• Sports tracking
• Wildlife monitoring

Remember

• YOLO is fast, R-CNN is accurate
• Need labeled bounding box data
• More data = better detection
• Use pre-trained models when possible