鉱山火災における煙の認識のためのエントロピー強化画像処理と改良されたYOLOv8の融合
Xiaowei Li1, Yi Liu1
1School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Entropy (Basel, Switzerland)
|August 28, 2025
まとめ
この研究では,鉱山火災の煙の正確な検出のための改良されたYOLOv8モデルを持つエントロピー強化画像処理法が導入されています. この技術は,複雑な地下環境で煙の可視性を高め,早期の火災検知と予防を改善します.
科学分野:
- コンピュータ・ビジョン
- 人工知能
- 鉱山安全工学
背景:
- 画像ベースの火災モニタリングは早期発見に不可欠ですが 鉱山環境では複雑な背景や模糊な特徴などの課題があります
- 既存の方法では 地下での検出の精度や強度に問題があり,誤った検出が起こります.
- 煙と背景情報の統合と 境界の不確実性は 煙の認識を複雑にします
研究 の 目的:
- 鉱山の火災で,画像ベースの技術を用いた,正確で堅牢な煙の認識方法を開発する.
- 煙と背景情報を統合し,視覚的な特徴を曖昧にするなど,複雑な地下環境の課題に取り組む.
- 鉱山火災のシナリオで煙の検出の精度とリコールを改善する.
主な方法:
- エントロピー強化画像処理と改良されたYOLOv8モデルを統合した新しい方法を提案した.
- 時空のエントロピー分離と,背景の騒音を抑制し,煙の明瞭性を高めるために,同距離のフレーム画像の微分融合法を使用した.
- YOLOv8mモデル内でエントロピー制限機能のフォーカシングメカニズムを導入し,変数スケールの煙のターゲットをより良く捉える.
主要な成果:
- 提案された方法は,シグナルとノイズの比率と煙の領域の明晰さを大幅に改善しました.
- 大規模な小規模な煙の標的のバランスのとれた正確な検出を達成しました.
- 精度,リコール,平均精度 (mAP) で優れたパフォーマンスを示し,ベースラインモデルおよび同様のアルゴリズムと比較して,リアルタイム検出の要求を満たしています.
結論:
- エントロピー強化のYOLOv8方法は,複雑な鉱山環境における煙の認識精度と強さを効果的に改善します.
- このアプローチは地下鉱山での火災の早期発見と予防に大きく貢献します.
- この方法は,従来の地上監視技術の限界を克服するための信頼性の高い解決策を提供します.
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