Object Detection
Computer vision task that identifies and locates objects within images or video, drawing bounding boxes around each detected item and classifying what it is.
In-Depth Explanation
Object detection combines classification (what) with localisation (where). It's essential for applications that need to find and identify multiple objects in complex scenes.
Detection vs classification:
- Classification: What's in this image? (one answer)
- Detection: What objects, and where? (multiple answers with locations)
Key architectures:
- Two-stage: Faster R-CNN, Mask R-CNN (accurate but slower)
- One-stage: YOLO, SSD, RetinaNet (fast, good accuracy)
- Transformer-based: DETR, DINO (newer approaches)
Detection outputs:
- Bounding boxes (x, y, width, height)
- Class labels with confidence scores
- Optional: segmentation masks, keypoints
Applications:
- Autonomous vehicles (pedestrians, vehicles, signs)
- Retail analytics (product detection, shelf monitoring)
- Security (person detection, object tracking)
- Manufacturing (defect and component detection)
Business Context
Object detection powers applications from retail shelf monitoring to warehouse inventory - automatically finding and counting items in images or video streams.
How Clever Ops Uses This
We implement object detection for Australian businesses in inventory management, quality inspection, and safety monitoring applications.
Example Use Case
"Retail analytics system detecting products on shelves, identifying out-of-stock items, and tracking planogram compliance automatically."