  Small object detection in aerial imagery presents significant challenges in
computer vision due to the minimal data inherent in small-sized objects and
their propensity to be obscured by larger objects and background noise.
Traditional methods using transformer-based models often face limitations
stemming from the lack of specialized databases, which adversely affect their
performance with objects of varying orientations and scales. This underscores
the need for more adaptable, lightweight models. In response, this paper
introduces two innovative approaches that significantly enhance detection and
segmentation capabilities for small aerial objects. Firstly, we explore the use
of the SAHI framework on the newly introduced lightweight YOLO v9 architecture,
which utilizes Programmable Gradient Information (PGI) to reduce the
substantial information loss typically encountered in sequential feature
extraction processes. The paper employs the Vision Mamba model, which
incorporates position embeddings to facilitate precise location-aware visual
understanding, combined with a novel bidirectional State Space Model (SSM) for
effective visual context modeling. This State Space Model adeptly harnesses the
linear complexity of CNNs and the global receptive field of Transformers,
making it particularly effective in remote sensing image classification. Our
experimental results demonstrate substantial improvements in detection accuracy
and processing efficiency, validating the applicability of these approaches for
real-time small object detection across diverse aerial scenarios. This paper
also discusses how these methodologies could serve as foundational models for
future advancements in aerial object recognition technologies. The source code
will be made accessible here.
