Object detection

Object detection refers to detecting instances of objects from a predefined class in images or video. It identifies the presence and location of objects for recognition. Key aspects:

Detects visual classes like people, cars, furniture.
Outputs bounding boxes around detected object instances.
Can detect multiple instances in a single image.
Relies on machine learning like convolutional neural networks.
Requires large amounts of labeled training data.
Popular datasets include COCO, PASCAL VOC.

Object detection underpins applications like face detection in cameras, medical image analysis, and self-driving vehicle systems.

State-of-the-art techniques build on region proposal models like R-CNN and use architectures like SSD and YOLO. Challenges include handling occlusion and small objects.

Robust object detection remains an active research area in computer vision, evolving alongside advances in deep learning.