個人用防具のデータセット
1Department of Industrial Engineering and Management, National Chin-Yi University of Technology No.57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 411030, Taiwan (R.O.C.).
Data in brief
|September 2, 2025
まとめ
高リスク産業における労働者の安全性を高めるため,パーソナル・プロテクト・エキペイメント (PPE) の標的検出に関する新しいデータセットが作成されました. このリソースは,建設現場のセキュリティと産業安全基準の改善に役立ちます.
科学分野:
- コンピュータ・ビジョン
- 労働安全と健康
- 機械学習
背景:
- 個人用保護具 (PPE) は,建設や製造などの危険な環境における労働者の安全にとって不可欠です.
- 既存の安全対策は,PPEの遵守を監視し,強制するための効果的なツールを必要とします.
- パーソナル・ペーパーの自動検出は,リアルタイムでの安全対策に不可欠です.
研究 の 目的:
- パーソナル・プロテクト・エキペイメント (PPE) の標的検出に関する包括的なデータセットを開発し導入する.
- パーソナル・エディション (PPE) のオブジェクト検出モデルの訓練と評価のための貴重なリソースを提供すること.
- 産業と建設の環境における労働者の安全とセキュリティを強化する.
主な方法:
- 2286枚の画像のデータセットが 様々な公共のプラットフォームと 現実世界のシナリオから集められました
- 画像は公式のYOLO (You Only Look Once) 仕様に従って注釈されました.
- このデータセットは,YOLOv8やYOLOv11のような一般的なオブジェクト検出フレームワークとの互換性のために設計されています.
主要な成果:
- ターゲット検出のための専門データセットが作成されました.
- このデータセットは,さまざまな画像ソースにより,幅広い表現を提供します.
- YOLO形式のアノテーションは,最先端のオブジェクト検出モデルとのシームレスな統合を確保します.
結論:
- 開発されたPPEデータセットは,労働安全の改善に大きく貢献します.
- パーソナル・ペーパー・エディション (PPE) の適合性を監視する自動化されたシステムの進歩を容易にする.
- このリソースは,産業安全基準の強化と職場での事故の減少につながると期待されています.
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