Anchor Based

Anchor-based methods are a cornerstone of object detection in computer vision, aiming to improve the accuracy and efficiency of identifying and locating objects within images and videos. Current research focuses on refining anchor assignment strategies, developing novel loss functions to enhance confidence score calibration and reduce uncertainty, and exploring alternative architectures like transformers to overcome limitations of traditional anchor-based detectors, particularly in challenging scenarios such as crowded scenes or tiny object detection. These advancements have significant implications for various applications, including autonomous driving, medical image analysis, and security systems, by improving the reliability and performance of object detection algorithms.