  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 https://github.com/hoiliu-0801/DNTR.
