PDSAM: Prompt-Driven SAM for Track Defect Detection

Track defect detection is critical for ensuring the safety and reliability of railway systems. Existing machine vision-based approaches are hindered by three key issues: high time complexity stemming from end-to-end network training, limited availability of training data (with only a few hundred labeled images), and suboptimal prediction precision. To address these challenges, this paper introduces the Prompt-Driven Segment Anything Model (PDSAM), a novel image semantic segmentation framework that introduces a paradigm shift in problem formulation. The core contribution lies in reformulating the segmentation task as a prompt generation problem, which offers two correlated advantages: first, a simplified prompt generation network reduces both training time and data requirements compared to standalone segmentation networks; second, an upscaling and visual prompting technique restores spatial resolution and mitigates the risk of local optima in feature optimization, enabling more precise and fine-grained segmentation outputs. Experimental evaluations on benchmark datasets demonstrate that PDSAM outperforms state-of-the-art methods in both prediction accuracy and computational efficiency for railway track defect detection. The proposed framework’s source code and pre-trained models are publicly available to facilitate reproducibility and further research, accessible at https://github.com/FreddyDylan/PDSAM/.