改善されたYOLOv8nモデルに基づく道路の明らかな亀裂検出の強化に関する研究
Wenyuan Xu1, Jianbo Xu1, Yongcheng Ji1
1School of Civil Engineering and Transportation, Northeast Forestry University, Harbin, Heilongjiang, China.
PloS one
|September 4, 2025
まとめ
この研究は,注意力メカニズムと高度な機能融合を統合することで,道路の亀裂検出のためのYOLOv8nを強化します. 最適化されたアルゴリズムは,道路表面の亀裂の識別に優れた性能を提供します.
科学分野:
- コンピュータ・ビジョン
- 人工知能
- 土木工学
背景:
- 道路表面の亀裂の検出はインフラストラクチャの維持に不可欠です.
- 既存のアルゴリズムは 変数的なクラックスケールと 複雑な背景で苦労しています
- 誤った検出は 保守の非効率化につながります
研究 の 目的:
- YOLOv8nアルゴリズムを改良し,道路表面の亀裂を検出する.
- 不十分な精度と検出を逃すという制限に対処するためです.
- より堅牢で効率的なクラーク識別システムを開発する.
主な方法:
- コンボリューションブロック・アテンション・モジュール (CBAM) とクロスステージ・パーチアル・フィーチャー・フュージョン (C2f) の統合
- 空間ピラミッド・プーリング・スピード・クロス・ステージ・パーシャル・チャネル (SPPFCSPC) の導入
- スリムネックパラダイムとウェイトド・インターセクション・オーバー・ユニオン (WIOU) 損失関数の採用
主要な成果:
- 強化されたYOLOv8nアルゴリズムはベンチマークと比較して有意な改善を示した.
- 平均精度 (mAP@50) は1. 8%,mAP@50-95は1. 7%,リコールは1. 8%増加しました.
- 洗練されたモデルは,優れた境界ボックスの回帰とグラデーションの緩和を示しています.
結論:
- 改善されたYOLOv8nアルゴリズムは,道路表面の亀裂検出の精度を効果的に向上させます.
- 改変はターゲットスケールの変動と背景の干渉をうまく処理します.
- この高度なアルゴリズムは,現代の道路整備の要求を満たしています.
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