无需标注数据，用视觉提示实现跨域道路损伤检测
Self-Supervised Visual Prompting for Cross-Domain Road Damage Detection
- 从无标签目标数据中生成缺陷感知提示，指导冻结的ViT模型
- 在四个基准上零样本迁移性能超越主流方法，少样本适应效率高
- 适合需要快速部署到新场景的智能巡检系统
自动化路面缺陷检测常因跨域泛化能力差而受限。监督检测器在本域表现强但需昂贵重标注，标准自监督方法虽捕捉通用特征，仍易受域偏移影响。本文提出 extit{PROBE}，一种无需标签的自监督框架，通过视觉提示探查目标域。引入自监督提示增强模块（SPEM），从无标签目标数据中提取缺陷感知提示以引导冻结的ViT骨干网络；设计域感知提示对齐（DAPA）目标，对齐提示条件下的源域与目标域表征。在四个挑战性基准上的实验表明， extit{PROBE}持续优于强监督、自监督及适应基线，实现稳健的零样本迁移、对域变化的更强鲁棒性，并在少样本适应中展现高数据效率。结果凸显自监督提示是构建可扩展、自适应视觉检测系统的可行方向。源代码已公开：https://github.com/xixiaouab/PROBE/tree/main
原文摘要 · Abstract (English)
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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