基于硬负意识锚点注意的密集物体检测

Sungmin Cho, Jinwook Paeng, Junseok Kwon
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引用次数: 1

摘要

本文提出了一种基于高级加权豪斯多夫距离(AWHD)和硬负感知锚点(HNAA)注意力的密集填充目标检测方法。高密度目标检测由于目标密度高、目标尺寸小,比传统的目标检测更具挑战性。为了克服这些挑战,提出的AWHD改进了传统的加权Hausdorff距离,并获得了精确的中心区域图。利用精确的中心区图,建议的HNAA关注确定每个锚点的相对重要性,并对硬负锚点施加惩罚。实验结果表明,我们提出的基于AWHD和HNAA注意的方法可以产生准确的密集填充目标检测结果,并且相对于其他先进的检测方法。代码可在${\color{Blue} \text{here}}$获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Densely-packed Object Detection via Hard Negative-Aware Anchor Attention
In this paper, we propose a novel densely-packed object detection method based on advanced weighted Hausdorff distance (AWHD) and hard negative-aware anchor (HNAA) attention. Densely-packed object detection is more challenging than conventional object detection due to the high object density and small-size objects. To overcome these challenges, the proposed AWHD improves the conventional weighted Hausdorff distance and obtains an accurate center area map. Using the precise center area map, the proposed HNAA attention determines the relative importance of each anchor and imposes a penalty on hard negative anchors. Experimental results demonstrate that our proposed method based on the AWHD and HNAA attention produces accurate densely-packed object detection results and comparably outperforms other state-of-the-art detection methods. The code is available at ${\color{Blue} \text{here}}$.
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