MASK R-CNN: A UNIFIED FRAMEWORK FOR OBJECT INSTANCE SEGMENTATION AND BEYOND

This paper presents a comprehensive evaluation of the Mask R-CNN framework for object detection and instance segmentation, with a focus on its implementation within the Detectron 2 library. By examining the architecture—including the Region Proposal Network, Feature Pyramid Network, and mask prediction head—the study highlights the model’s ability to perform accurate object localization and fine-grained segmentation. The research also explores how Detectron 2 enhances Mask R-CNN’s usability, performance, and flexibility for real-world applications such as autonomous driving and medical imaging. Through experiments on benchmark datasets, the paper demonstrates improvements in speed, precision, and scalability. Additionally, the paper proposes model enhancements within Detectron 2, showcasing its potential for customization and domain-specific optimization. This study serves as a valuable guide for researchers and practitioners aiming to leverage Mask R-CNN and Detectron 2 in advanced computer vision tasks.