DAP-CLIP: Dual Adaptive Prompting for multi-label zero-shot sewer pipe defect detection

Urban sewage pipe network health assessment is vital for public safety. However, traditional defect detection models, which rely on extensive expert annotations and operate under the closed-set assumption, fail to generalize when confronted with novel or unseen defects. Furthermore, real-world images often contain multiple co-occurring defects, highlighting the need for Multi-Label Zero-Shot Learning (ML-ZSL). We introduce the Dual Adaptive Prompting Network (DAP-CLIP), which builds on Contrastive Language–Image Pre-training (CLIP) and employs a Dual Adaptation (DA) strategy to systematically bridge the fine-grained industrial domain gap. This strategy includes Full Fine-tuning of the Visual Encoder (FFE) for domain-specific visual adaptation and a Multi-Granularity Prompting (MGP) mechanism that uses Domain Description Generation (DDG) for static local alignment and Vision-guided Prompt Fine-tuning (VPF) for dynamic global alignment. This DA mechanism ensures precise knowledge transfer from seen to unseen defect categories in sewer pipe scenes. Experiments on the Sewer-ML and TransPipe datasets demonstrate that DAP-CLIP outperforms existing ML-ZSL methods across key metrics, including mean average precision (mAP) and F1 score, confirming its robustness and practical value in pipe defect detection.