YOLO, which stands for 'You Only Look Once,' revolutionized object detection by treating it as a regression problem rather than a classification task. This unique approach allows YOLO to utilize a single convolutional neural network to predict bounding boxes and associated probabilities simultaneously, resulting in faster and more accurate detection compared to traditional methods that relied on multi-stage pipelines [3][4].

The algorithm achieves remarkable speed, processing images at about 45 frames per second while maintaining high mean Average Precision. This efficiency has made YOLO a top choice for real-time applications across various fields, including autonomous driving, surveillance, and medical imaging [1][2].

What did "YOLO" revolutionize in object detection?

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