SPAR-Det: Segmentation-guided and Prior-Aided Routing for Small Object Detection

Small Object Detection (SOD) is essential for real-world applications, including satellite image analysis and drone-based surveillance, where target objects typically exhibit limited spatial extent, high density, and visual similarity to complex backgrounds. These factors substantially hinder conventional object detection methods, resulting in weak feature representations and poor detection accuracy. To overcome these challenges, we introduce Segmentation-guided and Prior-Aided Routing for Small Object Detection (SPAR-Det), a unified framework integrating segmentation-guided attention, geometric prior supervision, and adaptive feature routing. At its core, SPAR-Det employs a Cross-Attention Heterogeneous Feature Fusion (CAHF) module that leverages pretrained segmentation backbones to enhance foreground object features while effectively suppressing background. Additionally, we propose a Geometric Prior Supervision Loss that combines Gaussian bounding box maps with segmentation feature maps, providing crucial geometric context and semantic cues to address the limited self-representation capability of small objects. Furthermore, our framework includes a Mixture-of-Experts (MoE) detection head, dynamically allocating specialized classifiers according to varying scene characteristics, thereby significantly improving generalization across diverse environments. Extensive evaluations conducted on two benchmark datasets, AI-TOD and VisDrone, demonstrate that SPAR-Det achieves state-of-the-art performance, verifying its robustness and applicability for challenging small object detection scenarios. The source code will be publicly released upon publication.