Boundary Distribution Estimation for Precise Object Detection

TL;DR

This paper proposes a novel boundary distribution estimation method that refines object bounding box edges for improved localization accuracy in object detection, addressing limitations of traditional center-based regression approaches.

Contribution

It introduces a boundary distribution estimation technique that enhances bounding box precision by focusing on object edges, backed by theoretical analysis and experimental validation.

Findings

Improved localization accuracy over traditional methods

Enhanced detector performance with boundary-focused approach

Demonstrated generalizability across datasets

Abstract

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.