Foreground-Guided and Geometry-Confusion-Aware Transformer for Damaged Component Detection in Urban Buildings

To address the need for detecting damaged regions that develop during the long-term service of urban landscape buildings, existing vision-based detection methods are easily affected by background interference such as shadows, decorative textures, and component joints in complex scenes. They also face substantial inter-class visual similarity and pronounced intra-class variations in target scale, aspect ratio, and appearance. To address these challenges, this paper proposes a Foreground-Guided and Geometry-Confusion-Aware Network (FGCNet). First, a foreground-guided wavelet modulation module constructs foreground–background contrast statistics for individual wavelet subbands and performs spatially constrained band modulation to enhance damage-related features with low visual contrast. Second, a Geometry-Confusion Aware Networkare class-prototype contrast branch mines hard negative classes using online confusion relationships from the base classifier, thereby improving discrimination between visually similar classes. Finally, a geometry-aware intra-class multi-prototype mechanism jointly exploits query appearance and predicted bounding-box geometry to adaptively model latent patterns within the same class. Experiments are conducted on an urban landscape building dataset containing 10 categories of damaged regions. The results show that FGCNet improves mAP50:95 and mAP50 over the baseline by 2.6 and 3.7 percentage points, respectively. The proposed method can provide technical support for visual inspection and damage-information recording in complex urban landscape building scenes.