YOLO, which stands for You Only Look Once, is an object detection algorithm that works by dividing an image into a grid of cells and predicting bounding boxes and class probabilities for each cell. It processes the entire image in a single forward pass through a neural network, making it fast and efficient for real-time applications.
Here's a simplified explanation of how YOLO works:
Grid Division: The input image is divided into a grid of cells, typically 7x7 or 13x13, depending on the version of YOLO.
Bounding Box Prediction: Each cell predicts a fixed number of bounding boxes, which are rectangles that enclose objects. Each bounding box has four coordinates (x, y, width, height) and a confidence score that indicates how likely it is to contain an object.
Class Probabilities: Each cell also predicts the probabilities of the objects belonging to each class (e.g., car, person, dog).
Concatenation: The bounding box coordinates, confidence scores, and class probabilities are concatenated into a single output tensor.
Non-Maximum Suppression: To eliminate duplicate detections, a non-maximum suppression algorithm is applied to remove overlapping bounding boxes with lower confidence scores.
Example: Imagine an image of a street scene. YOLO might divide this image into a 7x7 grid. Each cell in the grid will predict multiple bounding boxes and their associated confidence scores and class probabilities. For instance, one cell might predict a high-confidence bounding box for a car and a lower-confidence one for a pedestrian. After processing all cells, non-maximum suppression is used to refine the detections, resulting in a final set of bounding boxes for cars, pedestrians, and other objects in the image.
For real-time object detection applications, YOLO's speed and efficiency make it a popular choice. In the context of cloud computing, services like Tencent Cloud's AI Platform can leverage YOLO for scalable and efficient object detection tasks, enabling developers to integrate advanced computer vision capabilities into their applications without the need for extensive hardware resources.