Object detection algorithms (e.g., YOLO, SSD, Faster R-CNN)

• YOLO (You Only Look Once):

  • Concept: Single neural network predicts bounding boxes and class probabilities directly from full images.
  • Advantages: Fast inference speed suitable for real-time applications. Handles multiple objects per image well.
  • Disadvantages: May struggle with small objects due to its grid-based approach.

• SSD (Single Shot MultiBox Detector):

  • Concept: Similar to YOLO but uses multiple feature maps at different scales to detect objects.
  • Advantages: Good balance between speed and accuracy. Handles objects of different sizes better than YOLO.
  • Disadvantages: Can be slower than YOLO, especially with large numbers of object categories.

• Faster R-CNN (Region-based Convolutional Neural Network):

  • Concept: Two-stage detector: first proposes regions of interest (RoIs) and then classifies them.
  • Advantages: State-of-the-art accuracy. Offers flexibility in handling various object sizes and shapes.
  • Disadvantages: Slower than YOLO and SSD due to its two-stage architecture. More complex to implement and train.