Despite notable advancements in the field of computer vision, the precise
detection of tiny objects continues to pose a significant challenge, largely
owing to the minuscule pixel representation allocated to these objects in
imagery data. This challenge resonates profoundly in the domain of geoscience
and remote sensing, where high-fidelity detection of tiny objects can
facilitate a myriad of applications ranging from urban planning to
environmental monitoring. In this paper, we propose a new framework, namely,
DeNoising FPN with Trans R-CNN (DNTR), to improve the performance of tiny
object detection. DNTR consists of an easy plug-in design, DeNoising FPN
(DN-FPN), and an effective Transformer-based detector, Trans R-CNN.
Specifically, feature fusion in the feature pyramid network is important for
detecting multiscale objects. However, noisy features may be produced during
the fusion process since there is no regularization between the features of
different scales. Therefore, we introduce a DN-FPN module that utilizes
contrastive learning to suppress noise in each level's features in the top-down
path of FPN. Second, based on the two-stage framework, we replace the obsolete
R-CNN detector with a novel Trans R-CNN detector to focus on the representation
of tiny objects with self-attention. Experimental results manifest that our
DNTR outperforms the baselines by at least 17.4\% in terms of APvt on the
AI-TOD dataset and 9.6\% in terms of AP on the VisDrone dataset, respectively.
Our code will be available at this \href{this https
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提出了一种新的框架，DeNoising FPN with Trans R-CNN (DNTR)，用于改进微小目标检测的性能，通过引入 DN-FPN 模块和基于 Transformer 的 Trans R-CNN 检测器，结合对比学习和自注意力机制，实现了在 AI-TOD 数据集和 VisDrone 数据集上至少 17.4％和 9.6％AP 值的性能提升。