大学の研究室における安全リスク認識と責任追跡システムの研究と応用
Rundong Liu1, Yuxuan Ding1, Xiujin Zhu1
1School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
Sensors (Basel, Switzerland)
|February 13, 2026
まとめ
この研究は,大学の研究室の安全性のためのAIシステムを導入し,危険感と責任追跡を強化します. リスクのある行動の監視を向上させ,安全管理をより効率的かつ説明責任のあるものにします.
科学分野:
- コンピュータサイエンス コンピュータサイエンス
- 人工知能 (AI) とは,人工知能 (AI) のことです.
- 実験室の安全性について
背景:
- 大学の研究所の安全管理は,高周波リスク行動の監督と責任の追跡性を確保する上で課題に直面しています.
- 現在の方法は,しばしば"目に見えるが制御できない"行動と闘い,説明責任の問題につながります.
研究 の 目的:
- 実験室の安全に対する危険の認識と責任の追跡性のためのディープラーニングベースのシステムを提案する.
- 実験室の安全ガバナンスを強化するためのインテリジェント・テクニカル・ソリューションの開発.
主な方法:
- SEチャネル注意とNWD損失機能と統合されたYOLOv5sオブジェクト検出モデルを使用しました.
- マルチターゲットのトラッキングのためのDeepSORTを実装し,実験室環境でリアルタイムで危険感を得る.
- 総合的な管理システムのための責任マッチングアルゴリズムと視覚的なトレーサビリティメカニズムを開発しました.
主要な成果:
- 提案されたYOLO-labシステムは,実験場での平均平均精度 (mAP) を87.8%を達成しました.
- 優れたマルチターゲットの追跡能力を実証し,実験者がラボコートを着ていないことを追跡した.
- 伝統的な監督の限界に対処する責任の追跡報告を成功裏に生成しました.
結論:
- 開発されたシステムは,追跡可能で実証された説明責任を実現することによって,従来の実験室の安全監督の限界を効果的に解決します.
- 大学の研究室における安全ガバナンスを改善するための,知的で技術的な経路を提供します.
- 危険性の認識と追跡性を向上させ,より安全な研究環境に貢献します.
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