Weakly-supervised structural component segmentation via scribble annotations

Abstract

Segmentation of structural components in infrastructure inspection images is crucial for automated and accurate condition assessment. While deep neural networks hold great potential for this task, existing methods typically require fully annotated ground truth masks, which are time-consuming and labor-intensive to create. This paper introduces Scribble-supervised Structural Component Segmentation Network (ScribCompNet), the first weakly-supervised method requiring only scribble annotations for multiclass structural component segmentation. ScribCompNet features a dual-branch architecture with higher-resolution refinement to enhance fine detail detection. It extends supervision from labeled to unlabeled pixels through a combined objective function, incorporating scribble annotation, dynamic pseudo label, semantic context enhancement, and scale-adaptive harmony losses. Experimental results show that ScribCompNet outperforms other scribble-supervised methods and most fully-supervised counterparts, achieving 90.19% mean intersection over union (mIoU) with an 80% reduction in labeling time. Further evaluations confirm the effectiveness of the novel designs and robust performance, even with lower-quality scribble annotations.