TextureMeDefect：在移动设备上基于LLM的铁路部件缺陷纹理生成
TextureMeDefect: LLM-based Defect Texture Generation for Railway Components on Mobile Devices
AI总结：
本文提出TextureMeDefect，利用基于LLM的AI推理引擎在iOS和Android移动设备上交互式生成铁路部件逼真缺陷纹理，并通过多平台评估证明其在纹理质量、速度、成本和可用性方面优于传统工具。
摘要：
纹理图像生成已在游戏和娱乐等多种应用中得到研究。然而，面向工业应用的特定场景真实纹理生成，例如在铁路部件上生成缺陷纹理，仍处于未探索状态。一款适合移动设备、基于LLM并能生成细粒度缺陷特征的工具，可为理解实际发生缺陷所造成影响这一挑战提供解决方案。我们提出TextureMeDefect，这是一款创新工具，采用基于LLM的AI推理引擎。该工具允许用户在智能手机或平板电脑拍摄的铁路部件图像上，以交互方式创建逼真的缺陷纹理。我们开展了多方面评估，以考察在iOS和Android平台上使用该工具时所生成纹理的相关性、时间与成本。我们还分析了三种场景下的软件可用性评分（SUS）。TextureMeDefect通过更快地生成有意义的纹理，表现优于传统图像生成工具，展示了AI驱动的移动应用在消费级设备上的潜力。
英文摘要：
Texture image generation has been studied for various applications, including gaming and entertainment. However, context-specific realistic texture generation for industrial applications, such as generating defect textures on railway components, remains unexplored. A mobile-friendly, LLM-based tool that generates fine-grained defect characteristics offers a solution to the challenge of understanding the impact of defects from actual occurrences. We introduce TextureMeDefect, an innovative tool leveraging an LLM-based AI-Inferencing engine. The tool allows users to create realistic defect textures interactively on images of railway components taken with smartphones or tablets. We conducted a multifaceted evaluation to assess the relevance of the generated texture, time, and cost in using this tool on iOS and Android platforms. We also analyzed the software usability score (SUS) across three scenarios. TextureMeDefect outperformed traditional image generation tools by generating meaningful textures faster, showcasing the potential of AI-driven mobile applications on consumer-grade devices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
