Boundary Distribution Estimation for Precise Object Detection

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 fact...

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Bibliographic Details
Published inarXiv.org
Main Authors Peng Zhi, Zhou, Haoran, Huang, Hang, Zhao, Rui, Zhou, Rui, Zhou, Qingguo
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 19.07.2023
Subjects
Estimation
Flaw detection
Localization
Object recognition
Scaling factors
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Summary: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.
ISSN:2331-8422