Mask RCNN

Mask R-CNN is a two-stage deep learning model for instance segmentation, aiming to precisely identify and delineate individual objects within images. Current research focuses on improving its accuracy and efficiency, often comparing it to one-stage alternatives like YOLOv8, and exploring architectural modifications such as incorporating attention mechanisms and adapter tuning for parameter efficiency. Applications span diverse fields, including automated agricultural tasks, medical image analysis (e.g., detecting coronary artery stenosis), and industrial automation (e.g., robotic picking and waste sorting), highlighting its broad impact across various scientific and practical domains.