Tiny Object Detection

Tiny object detection focuses on accurately identifying and locating extremely small objects within images or videos, a challenging task due to limited pixel information and high susceptibility to noise. Current research emphasizes improved label assignment strategies, enhanced feature extraction using techniques like multi-scale detection and attention mechanisms, and the adaptation of existing architectures such as YOLO and DETR, often incorporating transformer networks or novel backbone designs. This field is crucial for diverse applications, including medical imaging (e.g., detecting pulmonary nodules), robotics (e.g., autonomous navigation), and remote sensing (e.g., identifying small objects in aerial imagery), with advancements driving improvements in accuracy and efficiency across these domains.