Attention Mechanism-Based Dense Upsampling of Transfer Learning Mask RCNN for Improved Object Segmentation

Object segmentation is a key task in computer vision, with applications ranging from self-driving to medical imaging. Leveraging deep learning approaches, notably Transfer Learning (TL), presents a potential path for improving segmentation accuracy and flexibility across a wide range of visual scenarios. However, standard TL-based algorithms often fail to capture complex object features and properly traverse complicated visual environments. In this paper, it presents a unique strategy that combines attention mechanisms with dense upsampling approaches within the Mask R-CNN framework, to improve the model's ability to distinguish fine-grained features and contextual cues that are critical for correct segmentation. Drawing on previous research initiatives, it expands on insights gained from studies investigating graph-based segmentation, tailored loss functions, and context-aware upsampling modules. The findings show excellent progress, with mean Average Precision (mAP) of 0.952, Average Recall at Precision=0.5 (RP@50) of 0.9173, Average Recall at Precision=0.75 (RP@75) of 0.8967, and an average AUC-ROC of 0.9368. These results demonstrate the effectiveness of the suggested technique for creating high-resolution segmentation masks while maintaining spatial information and adding contextual signals. Finally, the findings signal a considerable advance in object segmentation capabilities, opening the path for improved performance in a wide range of real-world applications.