多模式生成人工智能用于自动化路面状况评估:对模型性能进行基准测试.
1School of Planning, Design, and Construction, Michigan State University, East Lansing, Michigan, United States of America.
PloS one
|February 12, 2026
概括
生成型人工智能 (GAI) 模型可以使用街道水平图像评估道路路面状况. 推GPT-4o,因为它在路面管理中具有速度,准确性和成本效益的平衡.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 道路路面状况评估对于安全和维护至关重要.
- 传统的方法是昂贵的,缓慢的,难以扩展.
- 生成型人工智能为使用街道级图像进行视觉解释提供自动化解决方案.
研究的目的:
- 评估用于自动化道路表面状况评估的多式联运大型语言模型 (MLLMs).
- 为了比较专有和开源MLLM在路面管理任务上的性能.
- 为了确定最有效的MLLM,以实现成本效益和准确的路面分析.
主要方法:
- 测试了七个MLMLM (Gemini 2.5 Pro,OpenAI o1,GPT-4o,Gemma 3,Llama 3.2,LLaVA v1.6 Mistral,LLaVA v1.6 Vicuna) 进行了测试.
- 评估了应急识别,空间模式,严重程度和维护间隔的模型.
- 根据响应率,正确性,一致性,多模式错误和成本来评估性能.
主要成果:
- MLLMs可以解释街道平面图像用于路面评估.
- 模型以成本有效的方式产生与任务相关的输出.
- GPT-4o在响应性,准确性和计算成本方面取得了良好的平衡.
结论:
- 对于自动化路面状况评估来说,MLMLM是有前途的.
- 推GPT-4o作为其性能指标的首选模型.
- 这项技术可以优化道路维护投资,提高安全性.
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