舗装状態の自動評価のためのマルチモダルの生成AI:モデルパフォーマンスのベンチマーク
1School of Planning, Design, and Construction, Michigan State University, East Lansing, Michigan, United States of America.
PloS one
|February 12, 2026
まとめ
生成型人工知能 (GAI) モデルは,道路レベルの画像を使用して道路舗装の状態を評価することができます. GPT-4oは,歩道管理における速度,精度,費用対効果のバランスをとるため推奨されます.
科学分野:
- 土木工学 土木工学とは
- コンピュータサイエンス コンピュータサイエンス
- 人工知能 (AI) とは,人工知能 (AI) のことです.
背景:
- 道路舗装の状態の評価は,安全とメンテナンスに不可欠です.
- 伝統的な方法は,コストが高く,遅い,スケールが難しい.
- Generative AIは,ストリートレベルの画像を使用して視覚的な解釈のための自動化されたソリューションを提供しています.
研究 の 目的:
- 道路表面状態の自動評価のためのマルチモダル大言語モデル (MLLMs) を評価する.
- 舗装管理のタスクにおけるプロプライエタリとオープンソースのMLLMのパフォーマンスを比較する.
- 費用対効果の高い,正確な舗装分析のための最も効果的なMLLMを特定する.
主な方法:
- 7つのMLLM (Gemini 2.5 Pro,OpenAI o1,GPT-4o,Gemma 3,Llama 3.2,LLaVA v1.6 ミストラル,LLaVA v1.6 ビクナ) をテストしました.
- 緊急事態の識別,空間的なパターン,重症度,およびメンテナンス間隔に関する評価されたモデル.
- 応答率,正確性,一貫性,マルチモダルのエラー,コストに基づいてパフォーマンスを評価します.
主要な成果:
- MLLMは,舗装の評価のために路面レベルの画像を解釈することができます.
- モデルは,費用対効果的にタスクに関連した出力を生成します.
- GPT-4oは,応答性,精度,および計算コストの強いバランスを示しました.
結論:
- MLLMは,歩道の状態の評価を自動化するために有望であることを示しています.
- GPT-4oは,そのパフォーマンスメトリックの好ましいモデルとして推奨されています.
- この技術は,道路整備投資を最適化し,安全性を向上させることができます.
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