用自动生成提示的轻量模型，提升货车故障检测精度与实时性
Prompt-Driven Lightweight Foundation Model for Instance Segmentation-Based Fault Detection in Freight Trains
- 基于SAM自动生成任务提示，实现基础模型到铁路场景的高效迁移
- 在真实数据集上达到74.6的box AP和74.2的mask AP
- 轻量化设计适合边缘设备部署，适用于工业级实时检测
货运列车的精准视觉故障检测对智能交通系统维护至关重要，但复杂工况、结构重复部件及关键区域频繁遮挡或污染导致传统基于卷积神经网络和Transformer的实例分割方法泛化能力差、边界精度低。为此，我们提出一种面向货运列车故障检测的轻量级自提示实例分割框架。该方法基于Segment Anything Model，引入自提示生成模块，自动产生任务相关提示，实现基础模型知识向特定领域检测任务的有效迁移。同时采用Tiny Vision Transformer作为主干网络，降低计算开销，适配铁路监控系统中边缘设备的实时部署需求。我们构建了来自真实货运检查站的领域专用数据集，并进行大量评估。实验结果表明，该方法在该数据集上取得74.6 $AP^{ ext{box}}$ 和74.2 $AP^{ ext{mask}}$ 的性能，优于现有最先进方法，在准确率与鲁棒性方面均有提升，同时保持低计算开销。本工作为自动化货运列车检测提供了可部署、高效的视觉解决方案，展示了基础模型适配在工业级故障诊断场景中的潜力。
原文摘要 · Abstract (English)
Accurate visual fault detection in freight trains remains a critical challenge for intelligent transportation system maintenance, due to complex operational environments, structurally repetitive components, and frequent occlusions or contaminations in safety-critical regions. Conventional instance segmentation methods based on convolutional neural networks and Transformers often suffer from poor generalization and limited boundary accuracy under such conditions. To address these challenges, we propose a lightweight self-prompted instance segmentation framework tailored for freight train fault detection. Our method leverages the Segment Anything Model by introducing a self-prompt generation module that automatically produces task-specific prompts, enabling effective knowledge transfer from foundation models to domain-specific inspection tasks. In addition, we adopt a Tiny Vision Transformer backbone to reduce computational cost, making the framework suitable for real-time deployment on edge devices in railway monitoring systems. We construct a domain-specific dataset collected from real-world freight inspection stations and conduct extensive evaluations. Experimental results show that our method achieves 74.6 $AP^{\text{box}}$ and 74.2 $AP^{\text{mask}}$ on the dataset, outperforming existing state-of-the-art methods in both accuracy and robustness while maintaining low computational overhead. This work offers a deployable and efficient vision solution for automated freight train inspection, demonstrating the potential of foundation model adaptation in industrial-scale fault diagnosis scenarios. Project page: https://github.com/MVME-HBUT/SAM_FTI-FDet.git
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