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Densely-packed Object Detection via Hard Negative-Aware Anchor Attention

Authors
Cho, S.Paeng, J.Kwon, Junseok
Issue Date
Jan-2022
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
Large-scale Vision Applications Object Detection/Recognition/Categorization
Citation
Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022, pp 1401 - 1410
Pages
10
Journal Title
Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022
Start Page
1401
End Page
1410
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/55662
DOI
10.1109/WACV51458.2022.00147
ISSN
0000-0000
Abstract
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 ${\color{Blue} \text{here}}$. © 2022 IEEE.
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소프트웨어대학 (소프트웨어학부)
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