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Object Detection refers to algorithms designed for locating instances of objects in images or videos, assigning class labels to these object instances and producing bounding boxes around them. It is a key component of many computer vision applications like surveillance, robotics, autonomous vehicles, image retrieval and augmented reality. In the context of deep learning algorithms, object detection models often employ neural network architectures such as Faster R-CNN, YOLO (You Only Look Once), SSD (Single Shot Detector) or RetinaNet that utilize feature extraction and classification techniques to make predictions about objects in images or videos. These models are trained on large datasets with annotated object instances and bounding boxes, such as COCO (Common Objects in Context) or PASCAL VOC (Visual Object Classes), to learn object recognition patterns from visual data. The ability of deep learning algorithms to learn complex representations makes them particularly well suited for object detection tasks in images and video streams.