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Object detection is a difficult area of study in computer vision, but recent advances in deep learning and image processing have made it more accessible. Object detection models are adaptable and can be taught to recognize and find multiple objects. The process of creating item localizations often makes use of bounding boxes. Object detection is used in self-driving cars, object tracking, face detection and identification, robotics, and license plate recognition. The most popular object detection algorithms are Histogram of Oriented Gradients (HOG) and Convolutional Neural Networks (CNN).
