ディープラーニングベースの多層域異物検知に関する研究
Qingxue Liu1,2, Xia Wang3, Yun Su1,2
1School of Mechanical and Electrical Engineering, Kunming University, Kunming 650214, China.
Sensors (Basel, Switzerland)
|August 28, 2025
まとめ
CO-YOLOという新しいディープラーニングモデルは,小規模または遮断された標的の検出を向上させることで,送電線の安全性監視を強化します. この高度なモデルは,電力網の安全性アプリケーションに優れた精度と効率を提供します.
科学分野:
- 電気工学
- コンピュータ・ビジョン
- 人工知能
背景:
- ディープラーニングは電力網の安全性監視,特に送電線の危険性を検知するために不可欠です.
- 既存のモデルは,複雑なアーキテクチャと,小さな,または遮断されたターゲットを検出することで,リアルタイムおよびエッジデバイスアプリケーションを阻害する課題に直面しています.
研究 の 目的:
- 効率的で正確なディープラーニングモデルを開発し, 送電線における潜在的な安全上の危険を検出する.
- 小規模または遮断されたターゲットとエッジデバイスの展開における既存のモデルの限界に対処する.
主な方法:
- YOLOv11モデルとConvNeXtネットワークを統合し,ConvNeXt-You Only Look Once (CO-YOLO) モデルを作成しました.
- ハイパーパラメータチューニングのベイジアン最適化を活用してモデル収束を加速した.
主要な成果:
- CO-YOLOの平均精度 (mAP) は,0.5のIOU値で98.4%であり,0.5:0.95のIOU値で66.1%であった.
- このモデルは303のフレーム/秒 (FPS) を示し,YOLOv11とETLSH-YOLOの精度と効率の両方を上回った.
- CO- YOLOは,元のYOLOv11モデルと比較して,mAP@0. 5で1. 9%,mAP@0. 5: 0. 95で2. 2%の改善を示した.
結論:
- 提案されたCO-YOLOモデルは,送電線の安全性の危険を検出する精度と効率を大幅に高めています.
- CO-YOLOは,電力網の安全性アプリケーションにおけるリアルタイムモニタリングとエッジデバイスの展開のための有望なソリューションです.
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