Small Object

Small object detection and processing present significant challenges for computer vision systems, primarily due to limited visual information and susceptibility to noise and occlusion. Current research focuses on improving the performance of deep learning models, particularly convolutional neural networks (CNNs) and transformers, often incorporating techniques like attention mechanisms, data augmentation (including synthetic data generation), and novel loss functions (e.g., scale-adaptive IoU) to enhance accuracy and robustness. These advancements are crucial for applications ranging from medical image analysis (e.g., detecting small tumors) to autonomous driving (e.g., identifying small pedestrians) and industrial automation (e.g., precise robotic grasping of small parts). The development of more efficient algorithms is also a key area of focus, particularly for processing high-resolution 3D images.