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YOLOv5s-TC:YOLOv5sをベースにした,断熱器の故障検出のための改良されたインテリジェントモデル

Yingying Yin¹, Yunpeng Duan¹, Xin Wang¹

¹College of Information Technology, Jilin Agricultural University, Changchun 130118, China.

Sensors (Basel, Switzerland)

|August 28, 2025

まとめ

この研究では,無人航空機 (UAV) を使用して電力網の断熱器故障を検出するための改良されたディープラーニングモデルであるYOLOv5s-TCを導入します. 重要なネットワーク検査の検出精度と信頼性を大幅に高めます.

科学分野:

電気工学
コンピュータ・ビジョン
人工知能

背景:

断熱器は電力網の重要な構成要素であり,安全と運用の整合性のために信頼性の高い故障検出が必要です.
従来の手作業の検査は労働が多く誤りやすいため,高度な自動化ソリューションが必要になります
ディープラーニングと組み合わせた無人航空機 (UAV) 技術は,効率的な電力網の検査に有望なアプローチを提供します.

研究の目的:

電力網の検査の精度を高めるための高度なインテリジェント・アイソレーター検出モデルを開発する.
YOLOv5sのディープラーニングモデルを体系的に改善し,優れた故障検出能力を実現する.
提案されたモデルの性能を既存の最先端の方法と比較して検証する.

主な方法:

この研究では,YOLOv5sに基づいた強化された断熱器検出モデルであるYOLOv5s-TCを提案しています.
主な変更は,C3モジュールをボトルネックトランスフォーマーで置き換え,コンボリューションブロックアテンションモジュール (CBAM) を統合し,OSIoU損失関数を採用することです.
比較実験はYOLOv5s,Faster R-CNN,およびSSDモデルに対して行われました.

主要な成果:

YOLOv5s- TCは,ベースラインモデルと比較して有意な性能改善を示した.
平均精度 (mAP) は,YOLOv5sよりも4. 4%,Faster R-CNNよりも24. 5%,SSDよりも13. 9%向上した.
強化されたモデルは優れたターゲットローカライゼーションと機能学習能力を示しました.

結論:

提案されたYOLOv5s-TCモデルは,実用的な電力網検査アプリケーションに優れた検出性能と信頼性を提供します.
ボトルネック・トランスフォーマー,CBAM,OSIoUを統合することで,ディープラーニングモデルの精度が向上します.
この進歩は,自動化されたUAVベースの検査を通じて,より安全で効率的な電力網のメンテナンスに貢献します.

キーワード:

YOLOv5s についてコンピュータビジョンディープラーニング断熱器検出

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