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
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.
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.
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