S3-Det: Spectro-Spatial Synergistic Learning and Implicit Recursive Refinement for Efficient UAV Object Detection

Object detection in UAV imagery faces severe challenges arising from drastic scale variations, pervasive background noise, and the dominance of tiny objects, further compounded by the computational constraints of edge devices. Existing lightweight detectors frequently suffer from feature degradation, in which low-frequency background information tends to submerge thehigh-frequency details of small objects. To overcome this limitation, we propose S3-Det, a novel Spectro-Spatial Synergistic Detector. Specifically, we design the Spectro-Spatial Synergistic Network (S3Net) as the backbone architecture. Subsequently, we design an Implicit Recursive Feature Aggregator to enhance feature semantics without explicitly increasing network depth. By leveraging implicit refinement units with shared weights, this module recursively refines the feature pyramid. This mechanism achieves deep representational capacity with the parameter efficiency of a shallow network. Finally, we propose a decoupled detection head incorporating large-kernel context regression and SIoU loss to effectively alleviate the misalignment between classification and localization for tiny objects. Extensive experiments demonstrate that S3-Det achieves an exceptional trade-off between accuracy and latency, surpassing state-of-the-art lightweight detectors to establish a new benchmark for real-time aerial surveillance.