コンピュータビジョンによる古代都市壁のインテリジェントデフェクト検出
Gengpei Zhang1, Xiaohan Dou1, Leqi Li1
1School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434100, China.
Sensors (Basel, Switzerland)
|August 28, 2025
まとめ
この研究は 古代都市壁の欠陥を検出するための コンピュータービジョンの枠組みを紹介しています このシステムは画像の質を向上させ 裂け目や侵食を特定するために AI を使用し 文化遺産の保存を支援します
科学分野:
- 遺産保護科学
- コンピュータ・ビジョン
- デジタル遺産
背景:
- 古代の城壁は 重要な文化遺産の持ち主であり 環境要因や人間の活動による 裂け目や侵食などの欠陥に 容易く陥ります
- 効果的な保全には,これらの欠陥を特定し,対処するための高度なモニタリング技術が必要です.
- 既存の方法では 複雑な遺産に 必要な精度や包括的な分析が欠けています
研究 の 目的:
- コンピュータビジョンを用いた古代都市壁の多段階の欠陥検出フレームワークの開発と検証
- 裂け目,欠落したレンガ,塩の結晶化,植物の侵食などの様々な欠陥を特定する精度と効率を改善するためです.
- 歴史的建造物の知的モニタリングと保存のための技術的基盤を提供すること.
主な方法:
- 画像処理 (Restormer, SG-LLIE),欠陥サンプル増幅 (LFS-GAN),2D欠陥検出 (YOLOv12),深さ推定,および3D再構築 (ガウススプラッティング) を含むパイプライン.
- 画像の強化,欠陥認識,空間分析のための高度なAIモデルを使用しました.
- 3Dビジュアル化のためのガウスのスプラッティング.
主要な成果:
- このフレームワークは Jingzhouの古代城壁の 複数のタイプの欠陥を 発見しました
- 画像強化モデルは分析のための壁画像の品質を大幅に改善しました.
- 拡張データで訓練されたYOLOv12ネットワークは,効果的な2D欠陥認識を証明しました.
- 3D再構築は,検出された欠陥の詳細な視覚表現を提供しました.
結論:
- 提案されているコンピュータビジョンの枠組みは 古代の城壁の 知的モニタリングのための 強力な解決策を提供します
- この技術は,文化遺産の保護のための理論的・技術的な支援を不可欠にしています.
- 多段階のアプローチは,複雑な歴史的構造における欠陥検出の課題を効果的に解決します.
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