Object Detection with Deep Learning: YOLO, R-CNN, and Beyond

Object Detection Fundamentals

Object detection combines image classification with localization, identifying what objects are in an image and where they are.

Key Architectures

Two-Stage Detectors

#### R-CNN Family

R-CNN: Region proposals + CNN

Fast R-CNN: Shared convolutions

Faster R-CNN: Region Proposal Networks

Mask R-CNN: Instance segmentation

Pros: High accuracy

Cons: Slower inference

Single-Stage Detectors

#### YOLO (You Only Look Once)

YOLOv5/v8: Popular versions

Real-time performance

Good accuracy/speed balance

#### SSD (Single Shot Detector)

Multi-scale feature maps

Fast inference

Good for mobile

Transformer-Based

DETR

End-to-end detection

Attention mechanisms

Choosing the Right Model

For Accuracy

Faster R-CNN

Cascade R-CNN

DETR

For Speed

YOLO

SSD

EfficientDet

For Edge Deployment

YOLOv8-nano

MobileNet-SSD

TensorFlow Lite models

Training Custom Detectors

Collect and annotate data

Choose appropriate architecture

Configure hyperparameters

Train with augmentation

Evaluate on test set

Optimize for deployment

Evaluation Metrics

mAP (mean Average Precision)

IoU (Intersection over Union)

FPS (Frames per second)

Inference latency

Conclusion

Object detection has matured significantly, with options for every speed/accuracy trade-off.