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MSConv-YOLO:YOLOv8に基づく改良された小規模標的検出アルゴリズム

Linli Yang1,2, Barmak Honarvar Shakibaei Asli2

  • 1College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

Journal of imaging
|August 27, 2025
PubMed
まとめ

この研究は,MultiScaleConv-YOLO (MSConv-YOLO) を使用してドローンの画像で小さなオブジェクトを検出するためのYOLOv8を強化します. 改良されたモデルは 複雑な空中での小さな標的の 検出精度とリコールを強化します

キーワード:
MSConv-YOLO についてUAVの航空画像やってみよう小型ターゲット検出

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科学分野:

  • コンピュータ・ビジョン
  • 人工知能
  • リモートセンシング

背景:

  • 無人航空機 (UAV) の空からの画像では,スケールの変動と複雑な背景により,小さなオブジェクトの検出が困難です.
  • YOLOv8sのような既存のフレームワークは,小さなターゲットの最適性能のために強化する必要があります.

研究 の 目的:

  • UAVの航空画像の小型物体検出の性能を改善する.
  • YOLOv8sのフレームワークに実用的なエンジニアリングの強化を導入する.

主な方法:

  • 強化されたマルチスケール機能抽出のためのMultiScaleConv (MSConv) モジュールを統合することにより,MultiScaleConv-YOLO (MSConv-YOLO) を開発しました.
  • CIoUの損失を WIoU v3に置き換えて,小さな標的の制限ボックスの回帰を改善しました.
  • 高解像度検出ヘッドをネックヘッド構造に組み込み,細粒子の特徴を保存します.

主要な成果:

  • MSConv-YOLOは,VisDrone2019データセットのベースラインのYOLOv8sと比較して,mAP@0.5で6.9%の改善とリコールで6.3%の増加を達成しました.
  • アブラション試験は個々の強化の有効性を確認した.

結論:

  • MSConv-YOLOは,UAVのシナリオで小さなオブジェクトを検出するための実用的で効果的なソリューションを提供します.
  • 提案された改良は,YOLOv8sのアーキテクチャを根本的に変更することなく,検出パフォーマンスを改善します.