RSDNet: Res-CBAM-enhanced integration of shallow and deep networks for ultra-high-resolution image segmentation

In the field of ultra-high-resolution image segmentation, challenges persist regarding high resource consumption. Prior research, exemplified by ISDNet, introduced an innovative dual-branch network and a relation-aware feature fusion module. This module was designed to integrate shallow and deep features in a novel manner, resulting in improved inference speed while maintaining accurate segmentation. Our study aims to address the limitations of the original research and introduce enhancements. In this paper, we propose RSDNet, a Dual-Branch Feature Enhancement Framework for Super-Resolution Segmentation. Specifically, in our approach, the feature fusion module of ISDNet was replaced with a feature enhancement module. This module employs a feature addition method to combine deep and shallow features. Experimental results on the Inria Aerial dataset demonstrate a substantial improvement in the trade-off between memory usage and accuracy compared to the original method.