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関連する概念動画

Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the drone...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Bus Impedance Matrix01:24

Bus Impedance Matrix

Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

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マルチスケール機能に基づくインテリジェント・車両標的検出アルゴリズム

Aijuan Li1, Xiangsen Ning1, Máté Zöldy2

  • 1School of Automotive Engineering, Shandong Jiaotong University, Jinan 250357, China.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,YOLOv10オブジェクト検出モデルをインテリジェント・ドライビングに最適化し,そのサイズを11.8%削減し,同時に93.0%の精度を達成しました. 改良されたモデルは 自動運転システムの検出性能を向上させます

キーワード:
YOLOv10 についてインテリジェント車両マルチスケールフレキシブルコンヴォルション浅い補助核融合ターゲット検出

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科学分野:

  • コンピュータ・ビジョン
  • 人工知能
  • インテリジェントな輸送システム

背景:

  • オブジェクト検出モデルは,複雑なインテリジェントドライビングシナリオで誤った検出とミスされた検出でしばしば課題に直面します.
  • 既存のモデルは,高い計算負荷と大きなサイズがあり,リアルタイムのアプリケーションを制限します.

研究 の 目的:

  • YOLOv10アルゴリズムの最適化により,物体検出の精度が向上し,インテリジェント・ドライビングにおけるモデルの複雑性が低下します.
  • 自動運転車の検出の枠組みを より効率的かつ効果的に開発する.

主な方法:

  • マルチスケール・フレキシブル・コンボリューション (MSFC) を設計し,マルチスケール情報を同時に取得し,ネットワークの深さと計算コストを削減します.
  • 浅層補助融合 (SAF) と高度補助融合 (AAF) を使用して首のネットワークを再構築し,マルチスケール機能抽出を改善しました.
  • マルチスケールコンヴォルションとチャネル・アダプティブ・アテンション・メカニズムで 多様で正確な特徴の抽出を強化した.

主要な成果:

  • 最適化されたYOLOv10モデルは,オリジナルと比較して11.8%のファイルサイズを達成しました.
  • このモデルは93.0%の平均精度 (mAP@0.5) を達成し,優れた検出精度を示した.
  • 改善されたモデルは,全体的な性能,精度とサイズをバランスして,主流のオブジェクト検出モデルを上回りました.

結論:

  • 提案された改良は,インテリジェント・ドライビングアプリケーションのYOLOv10の性能を大幅に改善します.
  • この最適化されたモデルは,リアルタイムでオブジェクトを検出するための実用的なソリューションを提供し,計算リソースの減少と高い精度をバランスとします.
  • 開発されたフレームワークは,インテリジェントな運転シナリオの強力な検出システムを提供します.