Self-Supervised Visual Prompting for Cross-Domain Road Damage Detection

• URL: http://arxiv.org/abs/2511.12410v1
• Date: Sun, 16 Nov 2025 01:28:45 GMT
• Title: Self-Supervised Visual Prompting for Cross-Domain Road Damage Detection
• Authors: Xi Xiao, Zhuxuanzi Wang, Mingqiao Mo, Chen Liu, Chenrui Ma, Yanshu Li, Smita Krishnaswamy, Xiao Wang, Tianyang Wang,
• Abstract summary: ours is a self-supervised framework that emphvisually probes target domains without labels.<n>ours consistently outperforms strong supervised, self-supervised, and adaptation baselines.<n>These results highlight self-supervised prompting as a practical direction for building scalable and adaptive visual inspection systems.
• Score: 21.137567686181438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The deployment of automated pavement defect detection is often hindered by poor cross-domain generalization. Supervised detectors achieve strong in-domain accuracy but require costly re-annotation for new environments, while standard self-supervised methods capture generic features and remain vulnerable to domain shift. We propose \ours, a self-supervised framework that \emph{visually probes} target domains without labels. \ours introduces a Self-supervised Prompt Enhancement Module (SPEM), which derives defect-aware prompts from unlabeled target data to guide a frozen ViT backbone, and a Domain-Aware Prompt Alignment (DAPA) objective, which aligns prompt-conditioned source and target representations. Experiments on four challenging benchmarks show that \ours consistently outperforms strong supervised, self-supervised, and adaptation baselines, achieving robust zero-shot transfer, improved resilience to domain variations, and high data efficiency in few-shot adaptation. These results highlight self-supervised prompting as a practical direction for building scalable and adaptive visual inspection systems. Source code is publicly available: https://github.com/xixiaouab/PROBE/tree/main

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