YOLO (You Only Look Once) handles objects of different scales and shapes through several mechanisms.
Explanation:
- Multi - scale training and detection:
- YOLO uses a grid - based approach where the input image is divided into a grid of cells. Each cell is responsible for predicting bounding boxes and class probabilities for objects that are centered within it. For different scales, during training, it can use images with a variety of object sizes. For example, in a dataset containing pictures of both small cars and large buildings, the network learns to detect objects at different scales.
- In detection, it can apply different anchor boxes with various aspect ratios. Anchor boxes are predefined boxes of different sizes and shapes. For instance, for detecting both square - shaped objects like bricks and elongated objects like pencils, appropriate anchor boxes are used. The network adjusts the bounding box predictions relative to these anchor boxes.
- Feature extraction:
- The convolutional neural network (CNN) layers in YOLO extract features at multiple levels. Early layers capture low - level features like edges and corners which can be useful for detecting small - scale details of objects. Later layers capture more complex high - level features that are better for recognizing larger objects or objects with more complex shapes.
Example:
Consider a scene with a cat (small size), a person (medium size), and a car (large size). YOLO's multi - scale approach allows it to detect all of them. The small - scale features help in localizing the cat's outline, while the larger - scale features assist in defining the boundaries of the car. And for the person, the appropriate combination of feature levels and anchor boxes ensures accurate detection regardless of the shape variations such as different postures.
If you are looking for a cloud platform to deploy such object detection models efficiently, Tencent Cloud provides powerful computing resources and services. Its GPU - optimized instances can speed up the training and inference process of YOLO - like models. Also, Tencent Cloud's storage services can be used to store large datasets required for training these models.