サトイモ加工におけるリアルタイム欠陥検出のための最適化されたYOLOv8nベースモデル
Kan Luo1,2, Chuanshuai Jia3,4, Yu Chen4,5
1School of electronic, Electrical engineering and Physics, Fujian University of Technology, Fuzhou, 350118, China. luokan@fjut.edu.cn.
Scientific reports
|December 30, 2025
まとめ
本研究では、自動サトイモ加工欠陥検出のための修正YOLOv8nモデルを紹介し、高い精度と再現率で99%以上の精度を達成しています。効率的な深層学習アプローチは、産業処理の品質とリアルタイム機能を向上させます。
科学分野:
- 農業工学
- コンピュータビジョン
- 機械学習
背景:
- サトイモ加工は手作業に大きく依存しており、効率と品質の向上のためには自動欠陥検出が必要です。
- 従来のコンピュータビジョン手法は産業環境での精度に苦労していますが、深層学習モデルは計算集約的になる可能性があります。
主な方法:
- 特徴融合を強化するための双方向特徴ピラミッドネットワーク(BiFPN)を組み込んだ修正YOLOv8nアーキテクチャ。
- 計算複雑性を低減するためのVoV-GSCSPモジュールと共有パラメータ検出ヘッドの統合。
- 精度と堅牢性を向上させるためのWise Intersection over Union(WIoU)損失関数と広範なデータ拡張の利用。
結論:
- 提案された修正YOLOv8nモデルは、リアルタイムのサトイモ加工欠陥検出において優れた精度と計算効率を提供します。
- この進歩は、高スループットの品質管理を必要とする産業用途に最適です。
- この研究は、農業製品加工のための最適化された深層学習の可能性を強調しています。
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