Computer Vision
Object Detection — Teaching Computers to Find and Identify Objects
Object detection localizes and classifies objects in images, predicting both bounding boxes and class labels. It is one of the most impactful applications of deep learning.
- YOLO is Real-Time — Single-shot, grid-based prediction enables real-time detection at 30+ FPS
- Faster R-CNN is Accurate — Two-stage detector with Region Proposal Network for high-precision detection
- mAP is the Metric — Mean Average Precision over IoU thresholds is the standard evaluation measure
Object Detection — YOLO, Faster R-CNN, Anchor Boxes and mAP
Object detection localizes and classifies objects in images, predicting both bounding boxes and class labels. It is one of the most impactful applications of deep learning.
Detection vs. Classification
IoU (Intersection over Union)
Anchor Boxes
Non-Maximum Suppression (NMS)
Two-Stage: Faster R-CNN
Single-Stage: YOLO
Two-Stage vs Single-Stage
Evaluation: mAP
Summary
- Object detection predicts bounding boxes + class labels + confidence scores
- IoU measures overlap between predicted and ground truth boxes
- Anchor boxes provide reference shapes for regression
- NMS removes duplicate detections
- Two-stage (Faster R-CNN): accurate but slower
- Single-stage (YOLO): faster, real-time capable
- mAP is the standard evaluation metric
Next: Semantic Segmentation