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The arc length function represents the total distance traveled along a smooth curve measured from a fixed starting point to a variable endpoint. For curves that are continuous and differentiable, arc length provides a precise way to quantify distance when straight-line approximations are insufficient.To derive arc length, the curve is divided into many small segments. Each segment is approximated by a straight line whose length depends on the horizontal and vertical changes over that interval.
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A high-voltage power line spans a 40-meter horizontal distance between two transmission towers, resulting in a 10-meter vertical sag due to the effects of gravity and thermal expansion. The curve formed by the suspended cable is a catenary, which accurately models the behavior of a uniform, flexible cable under its own weight. Unlike a parabolic shape, the catenary is described by the hyperbolic cosine function and offers a precise representation of the cable's form.In this setup, engineers...
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The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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ATDIOU:对边界框回归的角差分损失函数.

Qiang Tang1,2, Hao Qiang1,2, Yuan Tian1,2

  • 1Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an 710119, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
概括

我们介绍了ATDIoU,这是一个新的边界框回归的角差分损失. 这种方法通过降低对定位错误的敏感性来提高对象检测的准确性,优于现有的方法.

关键词:
角-微分函数的角-微分函数界限框回归的边界框回归计算机视觉 计算机视觉对象检测检测对象检测对象检测

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 对象检测在计算机视觉中至关重要.
  • 边界盒回归 (BBR) 损失显著影响探测器性能.
  • 交叉与欧盟 (IoU) 基于的指标对位置偏差敏感,阻碍了优化.

研究的目的:

  • 提出ATDIoU,一个新的对边界盒回归的角差分损失.
  • 为了减轻界限框漂移,并减少对物体检测中的定位错误的敏感性.
  • 提高模型在学习目标位置的有效性.

主要方法:

  • 开发了ATDIoU,这是BBR的一种新的损失函数.
  • 使用二维角微分分布 (ATD) 建模预测和地面真相框顶点之间的距离.
  • 将ATDIoU集成到YOLOv6对象检测框架中.

主要成果:

  • 在对象检测任务中,ATDIoU表现得更好.
  • 在 PASCAL VOC 和 VisDrone2019 数据集上进行的实验.
  • 在各自的数据集上,与MPDIoU相比,实现了1.4%和0.7%的平均平均精度 (mAP) 提升.

结论:

  • ATDIoU有效地减轻了界限框漂移和定位错误.
  • 拟议的损失函数引导模型更准确地学习目标位置.
  • 在对象检测中,ATDIoU为界限框回归提供了一个有希望的进步.