2022 WACV WACV 2022

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

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 here.

🌉 Interdisciplinary Bridge — Computer Vision and Deep Learning
🧭 Keyword Pioneer — anchor attention
🐣 Hot Topic Early Bird — hard negative mining
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio