Prompt-Driven Lightweight Foundation Model for Instance Segmentation-Based Fault Detection in Freight Trains

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• The proposed method achieves 74.6 AP^box and 74.2 AP^mask on a domain-specific freight train inspection dataset.result 95%
• The method outperforms existing state-of-the-art methods in both accuracy and robustness while maintaining low computational overhead.result 90%
• The framework uses a Tiny Vision Transformer backbone to reduce computational cost for real-time deployment on edge devices.technical 90%
• The method introduces a self-prompt generation module that automatically produces task-specific prompts for the Segment Anything Model.method 90%
• The approach enables effective knowledge transfer from foundation models to domain-specific inspection tasks.method 85%
• The method is designed to address challenges of complex operational environments, structurally repetitive components, and frequent occlusions or contaminations in freight train inspection.technical 85%
• Potential challenges include scalability across diverse railway environments and integrity of prompt generation in varied lighting and weather conditions.limitation 80%
• The work demonstrates the potential of foundation model adaptation in industrial-scale fault diagnosis scenarios.market 80%

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