In principal modern detectors, the task of object localization is implemented by the box subnet which concentrates on bounding box regression. The box subnet customarily predicts the position of the object by regressing box center position and scaling factors. Although this approach is frequently adopted, we observe that the result of localization remains defective, which makes the performance of the detector unsatisfactory. In this paper, we prove the flaws in the previous method through theoretical analysis and experimental verification and propose a novel solution to detect objects precisely. Rather than plainly focusing on center and size, our approach refines the edges of the bounding box on previous localization results by estimating the distribution at the boundary of the object. Experimental results have shown the potentiality and generalization of our proposed method.

翻译：在主要的现代探测器中,物体定位的任务由集中于捆绑框回归的盒子子网执行。框子网通常通过递减框中心位置和缩放因子来预测物体的位置。虽然这种方法经常被采用,但我们发现,物体定位的结果仍然有缺陷,使探测器的性能不能令人满意。在本文件中,我们通过理论分析和实验核查来证明先前方法的缺陷,并提出了一个精确探测物体的新办法。我们的方法不是简单地侧重于中心与大小,而是通过估计在物体边界的分布来改进约束框对先前定位结果的边缘。实验结果显示了我们拟议方法的潜力和普遍性。