下水道パイプラインの欠陥検出のためのYOLOv8に基づく軽量クロススケール機能融合モデル
Ruibo Sha1, Zhifeng Zhang1, Xiao Cui1
1Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, Henan, China.
PloS one
|August 21, 2025
まとめ
この研究は,下水道パイプラインの欠陥検出のための改良されたYOLOv8モデルを導入します. 改良されたモデルは正確性と効率性を高め,実際のインフラストラクチャの検査に適しています.
科学分野:
- 土木工学
- コンピュータ・ビジョン
- 人工知能
背景:
- 下水管の整合性は 都市インフラにとって不可欠です
- 下水道欠陥の検出は,複数のスケールの問題,複雑な背景,照明の変動,および多様な欠陥タイプのために困難です.
研究 の 目的:
- 下水道パイプラインの欠陥検出のための軽量で正確なモデルを開発する.
- 下水道環境における既存のオブジェクト検出モデルの特徴抽出と融合能力を強化する.
主な方法:
- YOLOv8に基づいた軽量クロススケール機能融合モデルを提案した.
- 統合されたC2f-FAMとHS-BiFPNモジュールは,改良された多スケール特征抽出と意味的融合を可能にします.
- 複雑な環境で詳細を把握するために DySample を使用しました.
- データ増強を含む,下水道MLデータセットでモデルを訓練し,評価しました.
主要な成果:
- YOLOv8nモデルと比較して平均精度 (mAP) が3. 8%増加しました.
- モデルのパラメータを35%, 浮動小数点操作を21%, モデルのサイズを33%削減しました.
- 複雑な背景と様々な欠陥の形状を区別する能力が向上した.
結論:
- 提案されたモデルは,下水道パイプラインの欠陥を検出するための軽量で正確なソリューションを提供します.
- 機能融合と細部キャプチャの強化は,挑戦的な下水道環境で有効です.
- このモデルは,都市インフラストラクチャのメンテナンスにおける実用的な応用の可能性を大きく示しています.
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