YOLO (You Only Look Once) handles occluded and overlapping objects through its real-time object detection capabilities. When dealing with occlusion, YOLO uses a combination of techniques including multi-scale training and anchor boxes to predict object locations even when parts of the object are hidden. For overlapping objects, YOLO's convolutional neural network (CNN) architecture allows it to learn contextual information from the image, which helps in distinguishing between objects even when they overlap.
For example, in a scene with multiple people standing close together, YOLO can still identify each individual by analyzing the unique features of each person, such as their clothing or body shape, even if parts of their bodies are occluded by others.
In the context of cloud computing, services like Tencent Cloud's Object Storage COS can be used to store and manage large datasets required for training and deploying YOLO models efficiently. Additionally, Tencent Cloud's AI Platform provides tools and infrastructure to facilitate the development and deployment of deep learning models like YOLO for real-time object detection tasks.