SAM‐IND: Enhancing SAM With Implicit Neural Decoder for Structural Crack Detection

ABSTRACT Structural crack detection plays a pivotal role in infrastructure health monitoring, yet faces challenges in handling complex textures and maintaining cross‐domain generalization. This paper presents SAM‐IND, a novel framework that synergizes the segment anything model (SAM) with implicit neural representations to address these challenges. Our approach introduces two key innovations: (1) A parameter‐efficient adaptation strategy using low‐rank adaptation (LoRA) matrices, enabling domain‐specific feature learning while preserving SAM's generalization capabilities through frozen backbone parameters; (2) an implicit neural decoder that establishes continuous coordinate‐to‐segmentation mappings, effectively capturing high‐frequency crack patterns. Extensive experiments on three benchmark datasets demonstrate SAM‐IND's superior performance, achieving 89.63% F1 score on DeepCrack while requiring only 1.59M trainable parameters (1.75% of SAM's total). Notably, the model reduces cross‐material generalization error by 8.74% compared to state‐of‐the‐art methods, showing particular robustness in handling complex scenarios involving mottled surfaces and low‐contrast cracks. This work provides a new paradigm for structural health monitoring that balances accuracy, efficiency, and adaptability in real‐world inspection scenarios.