Small Object Detection

Small object detection focuses on accurately identifying and locating objects that occupy only a few pixels in an image, a challenging task due to limited visual information and often cluttered backgrounds. Current research heavily utilizes variations of the YOLO architecture, along with techniques like feature pyramid networks, attention mechanisms, and federated learning to improve detection accuracy and efficiency, particularly for resource-constrained devices. This field is crucial for applications ranging from assistive technologies for the visually impaired to autonomous driving and manufacturing quality control, driving advancements in both algorithm design and dataset creation. The development of robust and efficient small object detectors is essential for numerous real-world applications.