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Extraction: Advanced Methods00:56

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Concrete exhibits specific behaviors under different compressive loads. Understanding this is crucial for understanding its structural integrity. When concrete undergoes uniaxial compression, it tends to develop cracks that run parallel to the direction of the force. These parallel cracks stem from localized tensile stresses that occur perpendicular to the compression direction. Additionally, angled cracks may appear due to the formation of shear planes.
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CMOS 画像センサーの圧縮効率の良い特徴抽出方法

Keiichiro Kuroda1, Yu Osuka1, Ryoya Iegaki1

  • 1Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu-shi 525-8577, Japan.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
まとめ

この研究は,モノのインターネット (IoT) のためのバイナリ画像特性を抽出するための新しい方法を導入し,認識精度を保ちながら,データのサイズを99%大幅に削減します. これにより,エッジデバイスのための効率的で低消費電力の画像認識システムが可能になります.

キーワード:
CMOS画像センサは,CMOS画像センサとして使用されています.データ削減のデータ削減特徴の抽出 特徴の抽出オブジェクト認識のオブジェクト認識run-lengthエンコーディングを実行する.

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科学分野:

  • コンピュータビジョン コンピュータビジョン
  • 画像処理 画像処理
  • 組み込みシステム (Embedded System) とは,組み込みシステム (Embedded System) とは,組み込みシステムのことです.

背景:

  • 物事のインターネット (IoT) 時代の電力制約は,効率的なデータ処理を必要とします.
  • 従来の画像センサーは大量のデータを生成し,低電力アプリケーションに挑戦しています.
  • 既存の方法は,データサイズと認識精度とのトレードオフをしばしば含んでいます.

研究 の 目的:

  • CMOS画像センサーのための圧縮効率の良い特征抽出方法を開発する.
  • IoT時代の低電力画像認識システムを有効にするために.
  • 画像認識の精度を損なうことなく,データ伝送量を減らすために.

主な方法:

  • CMOS画像センサのための新しい特徴抽出方法を提案しました.
  • 抽出した6チャンネルバイナリ特征データ (明るさと水平のエッジ信号).
  • データ圧縮用のラン・レングス・エンコーディング (RLE) を採用した.

主要な成果:

  • エッジGPUのYOLOv7を使用したCOCOデータセットで60.7%のAPL50オブジェクト認識精度を達成し,データのサイズが99.2%減少しました.
  • MobileNetV3 を使用した Visual Wake Words (VWW) データセットで 89.4% の画像分類精度を達成し,データのサイズが 99.0% 減少しました.
  • 高精度を維持しながらデータサイズを大幅に減らすことで,従来の方法よりも優れたパフォーマンスを実証しました.

結論:

  • 提案された方法は,電力制限のIoT環境での画像認識のためのデータサイズを効果的に削減します.
  • このアプローチは,従来の精度データサイズトレードオフを克服します.
  • 効率的で低消費電力の画像認識システムの実用化を可能にします.