SMA-YOLO: エッジデバイスでのリアルタイムの車両検出の新しいアプローチ

Haixia Liu¹, Yingkun Song², Yongxing Lin^1,2

¹Keyi College, Zhejiang Sci-Tech University, Shaoxing 312369, China.

Sensors (Basel, Switzerland)

|August 28, 2025

まとめ

この研究は,インテリジェント・トラフィック・システムの効率的な車両検出モデルであるSMA-YOLOを紹介しています. 速度と精度を大幅に改善しサイズも縮小しエッジデバイスに最適です

科学分野:

コンピュータ・ビジョン
人工知能
機械学習

背景:

交通の管理と自動運転は車の正確な検出に大きく依存しています
現在のディープラーニングモデルは複雑な環境での速度,計算コスト,精度に問題がありエッジの展開を阻害しています

研究の目的:

エッジデバイスのリアルタイムアプリケーションのための軽量で効率的な車両検出モデルを開発する.
既存のYOLOv7モデルの検出速度と精度を向上させる.

主な方法:

提案されたSMA-YOLOは,MobileNetV3のバックボーン,SimAMの注意,ACONのアクティベーション,およびSIoUの損失をYOLOv7と統合しています.
コンピューティングの負荷を軽減し,機能抽出を強化するためにモデルの修正を実施しました.

主要な成果:

SMA-YOLOはモデルサイズ,計算要件,およびパラメータを大幅に削減しました.
検出速度が向上し,UA-DETRACデータセットで高い精度を維持しました.

結論:

SMA-YOLOは,組み込みシステムで効率的な車両検出を展開するための実現可能なソリューションを提供します.
提案されたモデルは,リアルタイムのインテリジェント輸送システムの性能と資源効率のバランスをとります.

キーワード:

ACONの起動機能モバイルネットV3 SimAM 注意力メカニズム YOLOv7 について軽量化車両検出

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