Object Detection

Object detection is an essential task in computer vision, which aims to identify objects of interest within an image or video.

One of the most popular approaches for object detection is using deep learning models such as

Faster R-CNN

and

YOLO

. These models are trained on large datasets such as

COCO

and

ImageNet

, which contain millions of labeled images to learn the features of objects and their relationships with the surrounding context.

Object detection models typically output a set of bounding boxes and associated class probabilities for objects detected in an image. These bounding boxes can be refined using post-processing techniques like non-maximum suppression to remove duplicate detections and improve accuracy.

Object detection has numerous applications, including surveillance, self-driving cars, and image search. For instance, self-driving cars use object detection to identify and track other vehicles, pedestrians, and traffic signs for safe navigation.

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