Object Detection
Object detection goes beyond classification: it identifies objects in an image and draws bounding boxes around them. Common tasks: detect pedestrians, cars, faces, or tumors in medical images.
Two Main Approaches
- Two‑stage detectors: First propose regions (Region Proposal Network), then classify each region. Accurate but slower. Examples: R‑CNN, Fast R‑CNN, Faster R‑CNN.
- One‑stage detectors: Predict bounding boxes and classes directly in one pass. Faster, suitable for real‑time. Examples: YOLO (You Only Look Once), SSD.
R‑CNN Family
- R‑CNN: extract 2000 region proposals, warp each, classify with CNN – very slow.
- Fast R‑CNN: share convolution for whole image, then ROI pooling – faster.
- Faster R‑CNN: Region Proposal Network (RPN) integrated – end‑to‑end trainable, good speed/accuracy tradeoff.
YOLO (You Only Look Once)
Divides image into grid, each cell predicts bounding boxes and class probabilities. Extremely fast (real‑time), used in autonomous driving and video surveillance. Versions: YOLOv3, YOLOv4, YOLOv5, YOLOv8.
SSD (Single Shot MultiBox Detector)
Like YOLO but uses feature maps at multiple scales to detect objects of different sizes. Good speed/accuracy balance.
Two Minute Drill
- Object detection = classification + localization (bounding boxes).
- Two‑stage (Faster R‑CNN): accurate, slower.
- One‑stage (YOLO, SSD): faster, real‑time.
- YOLO is widely used for real‑time applications.
Need more clarification?
Drop us an email at career@quipoinfotech.com