1. What is object detection in computer vision?
A: Object detection is a computer vision technique that identifies and locates objects within an image or video. It goes beyond image classification by detecting multiple objects and providing their locations using bounding boxes.
2. How does object detection differ from image classification?
A: Image classification assigns a single label to an entire image, identifying the presence of a specific object. Object detection, on the other hand, identifies multiple objects within an image and provides their locations, usually in the form of bounding boxes.
3. What are bounding boxes in object detection?
A: Bounding boxes are rectangular boxes drawn around objects detected in an image. They include labels that identify the object (e.g., “car” or “dog”) and a confidence score indicating the model’s certainty in its prediction.
4. What is Intersection over Union (IoU) in object detection?
A: IoU is a metric used to evaluate the accuracy of an object detector by comparing the overlap between the predicted bounding box and the ground truth bounding box. It is calculated as the ratio of the intersection area to the union area of the two bounding boxes.
5. What are some popular object detection algorithms?
A: Popular object detection algorithms include Single-Shot Detection (SSD), Region-Based Convolutional Neural Networks (R-CNN), You Look Only Once (YOLO), and Faster R-CNN. These algorithms vary in speed, accuracy, and complexity.
