YOLO (You Only Look Once) handles small object detection through several techniques:
Multi-scale Training: YOLO is trained on multiple scales, allowing it to detect objects of varying sizes. This helps in improving the detection of smaller objects by making the model more robust to different scales.
High-resolution Classifier: YOLOv3, for example, uses a high-resolution classifier (416x416 or 608x608) during training, which helps in better detection of small objects.
Feature Pyramid Networks (FPN): YOLOv4 incorporates FPN to enhance the detection of small objects by aggregating features from different layers of the network. This allows the model to capture both low-level and high-level features, which is crucial for small object detection.
Anchor Boxes: YOLO uses anchor boxes of various sizes and aspect ratios, which helps in detecting objects of different shapes and sizes, including small ones.
Data Augmentation: Techniques like random cropping, scaling, and flipping are used during training to artificially increase the diversity of small objects in the dataset.
Example: In a surveillance video, small objects like coins or buttons can be detected using YOLO by leveraging these techniques. The high-resolution classifier and FPN help in capturing the details of these small objects, while anchor boxes and data augmentation improve the model's ability to generalize to different scenarios.
For cloud-based solutions, Tencent Cloud offers services like Tencent Cloud AI, which provides advanced computer vision capabilities, including object detection. These services can be integrated with YOLO models to enhance small object detection in various applications.