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収穫ロボットの軽量ハイブリッドコンボリュートに基づくリアルタイム成熟ホーソーン検出ネットワーク
Baojian Ma1, Bangbang Chen1,2, Xuan Li1
1School of Mechatronic Engineering, Xinjiang Institute of Technology, Aksu 843100, China.
Sensors (Basel, Switzerland)
|August 28, 2025
まとめ
この研究は,自動収穫のための果樹園の課題を克服する,軽量なホーソーン検出モデルであるYOLO-DCLを導入します. 計算コストを削減して高い精度とリアルタイムのパフォーマンスを実現します.
科学分野:
- コンピュータ・ビジョン
- 農業ロボット
- 機械学習
背景:
- 自動収穫には リアルタイムのホーソンの検出が必要です
- 既存の方法は,標的の重複,葉の遮断,環境の変動に苦しんでおり,正確性と性能が低下しています.
- 高い計算能力は,現在の検出システムのリアルタイムアプリケーションを制限します.
研究 の 目的:
- 自動収穫の精度とリアルタイムのパフォーマンスを向上させるために,新しい軽量なホーソーン検出モデル (YOLO-DCL) を開発する.
- 標的に重なり 葉の遮断や ホーソンの果樹園の環境変化といった 課題に対処するためです
- エッジデバイスに適した計算効率の良いモデルを作成します.
主な方法:
- 提案されたYOLO-DCLモデルは,強化されたマルチスケール機能ピラミッドネットワーク (HSPFN) のバックボーンとコーディネートアテンス (CA) にダイナミックグループシャッフリングコンボリューション (DGCST) を統合しています.
- 軽量な検出ヘッドを使用し,コンヴォルションとバッチの正常化を共有し,複雑さを減らすためにPIoUv2の損失関数を使用しました.
- PySide6フレームワークを使用してNVIDIA Jetson Xavier NXエッジデバイスで検出システムを開発して展開しました.
主要な成果:
- YOLO-DCLは91. 6%の精度,90. 1%のリコール,および95. 6%の平均精度 (mAP) を達成した.
- モデルのサイズは 1.2 万のパラメータと 4.8 GFLOP の計算コストで 2.46 MB に削減されました.
- フィールドテストでは モデルの頑丈性,高精度,エッジデバイスでのリアルタイム機能が確認されました.
結論:
- YOLO-DCLは実用的な果樹園環境でホーソンをリアルタイムで検出するための 堅牢で効率的なソリューションです
- 軽量な設計と高い精度により 収穫ロボットに組み込むのに適しています
- この進歩により,ホーサーンの実用的な 自動収穫システムの開発が容易になりました
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