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軽量なYOLOv8nベースの人間の異常姿勢検出手法

Guilin Li1, Jiarui Zhang2, Qiyuan Ji1

  • 1School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, China.

Scientific reports
|February 4, 2026
PubMed
まとめ
この要約は機械生成です。

本研究では、異常な人間の姿勢を検出するための軽量アルゴリズムであるPSD-YOLOv8nを紹介し、ヘルスケアおよび安全アプリケーションにおける精度と効率を向上させます。このモデルはコンパクトなサイズで高いパフォーマンスを達成し、リアルタイム監視のための信頼性の高いソリューションを提供します。

キーワード:
注意機構人間の異常姿勢検出軽量YOLOv8n

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科学分野:

  • コンピュータビジョン
  • 人工知能
  • 生体医工学

背景:

  • 異常な人間の姿勢検出は、スマートヘルスケア、公共の安全、高齢者ケアにおいて非常に重要です。
  • 既存の手法は、オクルージョン、姿勢の変動、データの不均衡、モデルサイズの大きさなどの課題に直面しており、リアルタイムパフォーマンスを妨げています。

研究 の 目的:

  • 軽量で正確な人間の異常姿勢検出アルゴリズムを開発すること。
  • 現在の手法が抱える、複雑なシナリオやリソースが制約された環境での限界に対処すること。

主な方法:

  • YOLOv8nをベースとし、空間的およびチャネル的注意機構を備えたPoseMSAモジュールを組み込んだPSD-YOLOv8nアルゴリズムを提案しました。
  • 効率的な特徴抽出のために、分離可能な畳み込みと残差ボトルネックを利用しました。
  • 特徴的な関係モデリングを強化するために、キーポイント認識KA-SampleアップサンプリングモジュールとDetect-PSA検出ヘッドを導入しました。

主要な成果:

  • PSD-YOLOv8nは、SSHDatasetで97.8%のmAP@0.5および75.8%のmAP@0.5:0.95を達成しました。
  • ベースラインのYOLOv8nおよびYOLOv9-T、YOLOv10n、YOLOv11nなどの高度なモデルを上回りました。
  • モデルは2.07Mパラメータ、4.5MBの重みサイズというコンパクトさを誇り、軽量で高精度な機能を実証しました。

結論:

  • PSD-YOLOv8nは、異常な人間の姿勢検出のための効率的で信頼性の高いソリューションを提供します。
  • このアルゴリズムは、複雑な環境や多様な姿勢において堅牢性を示しました。
  • 軽量設計により、スマートヘルスケアおよび安全監視におけるリアルタイムアプリケーションに適しています。