Bounding Box

Bounding boxes, rectangular regions enclosing objects in images or videos, are fundamental to object detection and related computer vision tasks. Current research focuses on improving bounding box accuracy and robustness, particularly in challenging scenarios like those with limited data, noisy annotations, or complex backgrounds, often employing techniques like data augmentation, loss function refinement (e.g., IoU variations), and advanced model architectures such as YOLO and DETR variants. These advancements are crucial for applications ranging from autonomous driving and agricultural automation to medical image analysis and remote sensing, where accurate object localization is essential for reliable system performance and decision-making. The development of more accurate and efficient bounding box methods continues to be a significant area of active research.