In the field of state-of-the-art object detection, the task of object localization is typically accomplished through a dedicated subnet that emphasizes bounding box regression. This subnet traditionally predicts the object's position by regressing the box's center position and scaling factors. Despite the widespread adoption of this approach, we have observed that the localization results often suffer from defects, leading to unsatisfactory detector performance. In this paper, we address the shortcomings of previous methods through theoretical analysis and experimental verification and present an innovative solution for precise object detection. Instead of solely focusing on the object's center and size, our approach enhances the accuracy of bounding box localization by refining the box edges based on the estimated distribution at the object's boundary. Experimental results demonstrate the potential and generalizability of our proposed method.

翻译：在先进目标检测领域，目标定位任务通常通过专门负责边界框回归的子网络完成。该子网络传统上通过回归框的中心位置和缩放因子来预测目标位置。尽管该方法已被广泛采用，但我们观察到定位结果常存在缺陷，导致检测器性能不尽如人意。本文通过理论分析与实验验证，揭示了以往方法的不足之处，并提出了一种面向精确目标检测的创新性解决方案。我们的方法并非仅关注目标的中心与尺寸，而是基于目标边界估计分布来优化框边缘，从而提升边界框定位精度。实验结果证明了该方法具有潜力与泛化能力。