Object Detection is a computer vision technique used to identify and localize objects in an image or video. It is used to detect objects such as people, cars, buildings, and other objects in an image or video.
Object detection is a computer vision technique used to identify and localize objects in an image or video. It is a form of supervised learning, where a model is trained to detect objects in images or videos. The model is trained using labeled images, which are images that have been labeled with the objects they contain. The model is then used to detect objects in new images or videos.
Object detection is used in a variety of applications, such as autonomous vehicles, facial recognition, and medical imaging. In autonomous vehicles, object detection is used to detect other vehicles, pedestrians, and obstacles in the environment. In facial recognition, object detection is used to detect faces in images or videos. In medical imaging, object detection is used to detect tumors or other abnormalities in medical scans.
Object detection is a complex task, as it requires the model to identify objects in a variety of shapes, sizes, and orientations. To achieve this, object detection models use a variety of techniques, such as convolutional neural networks, region-based convolutional neural networks, and single-shot detectors.
Object detection is an important and rapidly evolving field of computer vision, and it has a wide range of applications. It is used in a variety of industries, from autonomous vehicles to medical imaging, and it is an important tool for understanding the world around us.