Fusing Dual-Threshold Prompts with SAM for Shot Peening Coverage Assessment on Aircraft Propeller Blades

Shot peening is a critical surface treatment for improving the fatigue resistance of aircraft propeller blades operating under complex cyclic loads. While accurate coverage evaluation is essential for quality assurance, its development is severely hindered by a fundamental bottleneck: the extreme scarcity of annotated datasets in this niche aerospace domain, where data collection is costly and low-frequency, as each acquisition requires the actual peening of high-value components. Consequently, existing practices are restricted to subjective manual inspection or conventional segmentation methods that lack robustness under complex textures. To bridge this gap, this study develops an integrated automated surface evaluation framework, termed DT-ZSAM (Dual-Threshold Zero-shot Assessment Model), which circumvents the data-dependency bottleneck by leveraging the zero-shot capabilities of the Segment Anything Model (SAM) within a custom-designed prompt-generation pipeline. To ensure end-to-end automation without manual intervention, the framework identifies candidate regions via a dual-threshold scheme in grayscale and brightness domains and extracts representative prompt points through density-based analysis refined by DBSCAN clustering. Experimental results demonstrate that the proposed framework achieves precise segmentation without requiring any pixel-level annotated training data. Notably, the proposed framework yielded a coverage rate of 30.57%, aligning closely with the expert visual consensus (25–35%), whereas the standard commercial instrument (TCV-2A) significantly overestimated the coverage at 62.33% due to its sensitivity to surface textures and fixed calibration logic. This framework provides a robust and pragmatic solution for high-stakes industrial quality control, offering a reliable path for automating inspection in domains where large-scale data acquisition is practically unfeasible.