Research on railway large vision model
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摘要:
研究铁路计算机视觉大模型关键技术及其应用，对统筹和促进铁路人工智能发展具有深远意义。文章依托铁路人工智能平台的算力与大模型支撑组件，提出从基础大模型到铁路计算机视觉大模型，再到铁路计算机视觉场景大模型的架构设计思路。基于基础大模型，设计模型训练框架，运用模型剪枝和多尺度推理技术保障推理速度与精度，完成铁路计算机视觉大模型的构建；提出铁路计算机视觉大模型的应用场景，并选取线路环境安全管控智能识别场景对该大模型能力进行验证。实验结果表明，铁路计算机视觉大模型在复杂背景下的微小目标检测方面表现卓越，具有较好的应用前景，将在铁路运输安全、移动装备检测、铁路客货运服务等业务领域发挥更加重要的作用。
Abstract:Studying the key technologies and applications of railway large vision model has profound significance for coordinating and promoting the development of railway artificial intelligence. This paper relied on the computing power and large model support components of the railway artificial intelligence platform to propose an architecture design concept from the basic large model to the railway large vision model and then to the railway scenario large vision model. Based on a basic large model, the paper designed a model training framework, and used model pruning and multi-scale reasoning techniques to ensure inference speed and accuracy, implemented the construction of a railway large vision model. The paper also proposed the application scenarios of railway large vision model, and selected the intelligent recognition scenario of line environment safety management and control to verify the capabilities of this large vision model. The experimental results show that the railway large vision model performs excellently in detecting small targets in complex backgrounds and has good application prospects. It will play a more important role in railway transportation safety, mobile equipment detection, railway passenger and freight transport services, and other business domain.
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表 1 实验环境配置
硬件配置 型号/规格 CPU Intel（R） Xeon（R） Gold 5218 CPU @ 2.30 GHzGPU NVIDIA GeForce RTX 6000Ada 显卡显存 48 G 操作系统版本 Ubuntu 18.04.6 LTS CUDA 11.7 
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表 3 实验结果对比
模型类别 mAP50 mAP50:95 推理速度/(ms·张−1) 铁路计算机视觉大模型 87.42 70.91 357.5 铁路计算机视觉大模型（模型剪枝后） 85.35 65.81 201.7 YOLOv8 81.35 61.49 30.2 Faster R-CNN 66.31 39.94 68.2 ViT-Adapter-L 86.25 63.72 302.3 铁路计算机视觉场景大模型 94.64 77.48 341.5 铁路计算机视觉场景大模型
（模型剪枝后）92.85 75.61 198.7
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