What is Object Detection in Computer Vision?
Updated Feb 20, 2026
Short answer
Object detection identifies objects in an image and also locates them using bounding boxes.
Deep explanation
Computer Vision extends beyond classification through object detection, which not only tells what objects are present but also where they are in the image.
Object detection models output:
- Class label (what the object is)
- Bounding box coordinates (where the object is located)
Modern detection systems like YOLO or Faster R-CNN divide the image into regions or use anchor boxes to predict multiple objects at different scales. The model learns both spatial and semantic features to accurately locate objects even when they overlap or appear in different sizes.
This makes object detection more complex than classification because it requires understanding both “what” and “where” simultaneously.
Real-world example
A self-driving car detecting pedestrians, traffic lights, and other vehicles in real time uses object detection to understand its surroundings and make driving decisions.
Common mistakes
- - Confusing detection with classification (detection includes location).
- - Assuming it works equally well for small and large objects without training adjustments.
- - Ignoring performance trade-offs between speed and accuracy in models.
Follow-up questions
- What is YOLO and how does it work?
- What are anchor boxes?
- What is Non-Max Suppression?