Object detection lies at the heart of all applications employing computer vision. Identifying what the objects are and where they are in an image precedes any processing or operation . Images had to be scanned multiple times before they could be processed, until YOLO emerged. The You Only Look Once Algorithm (YOLO) was the breakthrough that enabled real time object detection. 

YOLO is a deep learning based object detection algorithm that adopts a “one-look” method and it processes the entire image in a single forward pass of a neural network. What makes it so fast is its method of dividing the image into a grid and simultaneously predicting the bounding boxes and class probabilities from the entire image. 

Given its speed, YOLO is widely used in applications for surveillance and security, autonomous vehicles and medical imaging. 

As capable as it is, YOLO can struggle with small or densely packed objects. It has also been found to be less accurate than some slower detection models in certain scenarios. 

Later versions(YOLOv12, YOLOv13) however , have addressed these drawbacks and have further improved its speed and accuracy. YOLO is also working in combination with transformers in hybrid models and plays an instrumental  role in real-time AI applications.