S2FCNet: Semantic and Spatial Feature Compensation Network for Tiny-Object Detection

Tiny-object detection (TOD) in remote sensing images remains an extremely challenging task, primarily due to the severely limited feature availability and susceptibility to interference from complex backgrounds. Recently, the multiscale feature-based methods have demonstrated effectiveness in TOD. However, they often neglect that the low-level features struggle to activate the discriminative local semantics and lack global semantic information due to the limited local receptive fields. To address these issues, this paper proposes a semantic and spatial feature compensation network (S2FCNet) for TOD. To mitigate the gradual degradation of semantic information from high-level to low-level features in multiscale representations, we propose a local semantic reactivation module (LSRM), which reactivates low-level local semantic features through top-down guidance from high-level semantic features. To enhance spatial perception capabilities, we develop a foreground spatial sense module (FSSM) that captures precise spatial location information, effectively suppresses the background noise, and enhances the foreground features. Meanwhile, we introduce a spatial guidance mechanism (SGM) to compensate for the loss of spatial awareness caused by downsampling operations. Additionally, we synergistically combine semantic and spatial features through a multilevel fusion mechanism (MLFM), enabling more accurate detection of tiny objects. Extensive experiments on three challenging datasets demonstrate the effectiveness and superiority of the S2FCNet in comparison with the state-of-the-art methods. The code will be released at https://github.com/DetectionTiny/S2FCNet