軽量なSCL-YOLOv8:伝送線外物検出のための高性能モデル
Houling Ji1, Xishi Chen1, Jingpan Bai1
1School of Computer Science, Yangtze University, Jingzhou 434023, China.
Sensors (Basel, Switzerland)
|August 28, 2025
まとめ
この研究は,UAVを使用した伝送線でリアルタイムで異物検出のための軽量アルゴリズムであるSCL-YOLOv8を導入します. コンピューティングの負荷とモデルのサイズを大幅に減らし,検出の精度を向上させ,電力網の安全性を向上させます.
科学分野:
- 電気工学
- コンピュータ・ビジョン
- 人工知能
背景:
- 送電線は外来物体からの侵入のリスクがあり,潜在的に停電を引き起こす可能性があります.
- 現在のUAVによる検査は,データ伝送の必要性のためにリアルタイムの分析と効率が欠けている.
- 現存するYOLOモデルは 効率的なUAV展開には 計算が過密です
研究 の 目的:
- UAVプラットフォームのリアルタイム分析のための軽量な異物検出アルゴリズムを開発する.
- 送電線での異物検出の効率と精度を向上させる.
- UAVのオブジェクト検出システムの計算の複雑さとモデルのサイズを減らす.
主な方法:
- 提案されたSCL-YOLOv8は,YOLOに基づいた新しい軽量検出アルゴリズムです.
- CSPDarknet53のバックボーンをStarNetに置き換えて コンピューティングの複雑さと効率的な機能抽出を可能にしました
- CGLU-ConvFormerモジュールを導入し,マルチスケールとローカルな特徴の抽出を強化しました.
- 検出ヘッドを改良し,コンボリューションレイヤを共有し,グループノーマライゼーションにより,計算を削減し,機能の融合を改善しました.
主要な成果:
- SCL- YOLOv8は平均精度 (mAP@0. 5) を94. 2%を達成した.
- YOLOv8nと比較して,モデルのパラメータを56.8%,浮動点操作 (FLOP) を45.7%,モデルのサイズを50%大幅に削減した.
- 検出精度が向上し,計算能力が低下しました.
結論:
- SCL-YOLOv8は,UAVを介して伝送線でリアルタイムで異物検知するための効率的で正確なソリューションを提供します.
- 軽量な設計により,リソースの制限のあるUAVプラットフォームに展開できます.
- この進歩は,電力網の安全性と運用効率の向上に貢献します.
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