TextureMeDefect: LLM-based Defect Texture Generation for Railway Components on Mobile Devices

Autor:	Ferdousi, Rahatara, Hossain, M. Anwar, Saddik, Abdulmotaleb El
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Graphics Computer Science - Human-Computer Interaction
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 Pages, 8 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.18085 Zobrazit plný text záznamu View this record from Arxiv