What is object detection?

Explain different approaches to object detection including R-CNN, YOLO, and SSD.

Object Detection Overview

Object detection is a computer vision task that combines object classification (what) with localization (where). It aims to identify and locate multiple objects in an image by drawing bounding boxes around them and classifying each object.

Key Approaches:

1. Region-based CNN (R-CNN) Family:

• R-CNN (2014):

  • Uses selective search to propose ~2000 regions
  • Runs CNN on each region separately
  • Classifies with SVM
  • Very slow but pioneering approach

• Fast R-CNN (2015):

  • Single CNN pass on whole image
  • ROI pooling for feature extraction
  • 10x faster than R-CNN
  • Still relies on external region proposals

• Faster R-CNN (2015):

  • Introduces Region Proposal Network (RPN)
  • End-to-end trainable
  • Real-time capable (5-17 FPS)
  • Industry standard for accuracy

2. Single-Shot Detectors:

• YOLO (You Only Look Once):

  • Divides image into grid cells
  • Single network predicts all objects
  • Very fast (45-155 FPS)
  • Multiple versions (v1-v8) with improvements
  • Best for real-time applications

• SSD (Single Shot Detector):

  • Uses multiple feature maps
  • Predicts fixed set of boxes
  • Good speed-accuracy balance
  • Better with small objects than early YOLO

3. Modern Developments:

• Transformer-based:

  • DETR: Uses transformers for detection
  • No need for anchor boxes
  • Clean, modern architecture

• Mobile-optimized:

  • MobileNet-SSD
  • YOLOv8-nano
  • Efficient for edge devices

Key Components:

1. Backbone Network:

   • Feature extraction (ResNet, VGG)
   • Transfer learning common

2. Detection Head:

   • Classification
   • Bounding box regression

3. Post-processing:

   • Non-maximum suppression (NMS)
   • Confidence thresholding

Common Applications:

• Autonomous vehicles
• Surveillance systems
• Medical imaging
• Retail analytics
• Manufacturing quality control

The choice of detector depends on specific requirements for speed, accuracy, and available computational resources.