SCASNet: Spatial Context-Aware Selection Network for Small Object Detection in Aerial Imagery

The detection of small objects within intricate backgrounds poses a significant challenge in the domain of aerial image object detection. In this manuscript, a spatial context-aware selection network (SCASNet) is proposed, which innovatively integrates a state space model with the YOLO architecture to address this challenge. A spatial selection block and a context-aware block are designed to form a spatial context-aware selection module, which can overcome the limitations of the original state space model in sequence modeling, such as insufficient receptive fields and weak local dependency modeling. Then, a channel prior multidimensional attention enhancement module is proposed to focus on key information and optimize the extraction of spatial relationships. It leverages multiscale strip convolutions to map spatial relationships and dynamically allocates weights across channel and spatial dimensions. Finally, a content-focused attention module is designed in the detection heads to fuse fine-grained features from the lower layers of the backbone network with semantic features from the neck layers, which enhances the richness of feature representation. Extensive experiments conducted on publicly available datasets, VisDrone, AI-TOD, and SSDD, demonstrate the competitive performance of the proposed SCASNet compared with existing aerial image object detection models.