Hilbert Curve-Based Attention Enabling Topology-Preserving Image Tensor Representation for Semantic Segmentation Network

Drone-based building defect segmentation remains challenging due to complex surface textures and illumination variations. We propose TPSegformer, a topology-preserving segmentation framework that mitigates mis-segmentation in such scenarios. Its decoder incorporates a Hilbert curve–based topology-preserving mechanism to maintain spatial continuity and boundary precision during category layer computation. A lightweight multi-scale fusion module enhances semantic representation, while global context modeling strengthens holistic perception. Experiments on the building defect dataset show that TPSegformer outperforms existing segmentation methods, achieving 80.77\% mIoU and 90.22\% Acc. On the Dacl10k dataset, it maintains strong generalization, reaching 44.27\% mIoU and 60.32\% Acc across diverse materials and defect types.