Summary of Texturemedefect: Llm-based Defect Texture Generation For Railway Components on Mobile Devices, by Rahatara Ferdousi et al.
TextureMeDefect: LLM-based Defect Texture Generation for Railway Components on Mobile Devices
by Rahatara Ferdousi, M. Anwar Hossain, Abdulmotaleb El Saddik
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Texture image generation has been explored in various domains, including gaming and entertainment. However, realistic texture generation for industrial applications, such as defect textures on railway components, remains unaddressed. To address this challenge, we introduce TextureMeDefect, a mobile-friendly, LLM-based tool that generates fine-grained defect characteristics. This tool allows users to create realistic defect textures interactively on images of railway components taken with smartphones or tablets. Our evaluation assessed the relevance of generated texture, time, and cost using TextureMeDefect on iOS and Android platforms, as well as software usability score (SUS) across three scenarios. Our results demonstrate that TextureMeDefect outperformed traditional image generation tools by generating meaningful textures faster. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a tool that can create realistic images of defects on railway components using just your smartphone or tablet! That’s what we created with TextureMeDefect, a special AI-powered tool. We wanted to help people understand the impact of defects in real-life situations. Our tool lets users make these images interactively, which is faster and more cost-effective than traditional methods. We tested it on different devices and found that it works really well. This technology has huge potential for helping industries like manufacturing and construction. |
Keywords
» Artificial intelligence » Image generation
