FeedbackSTS-Det: Sparse-Frames-Based Spatio-Temporal Semantic Feedback Network for Moving Infrared Small Target Detection

Infrared small target detection (ISTD) has been a critical technology in various civilian and industrial applications over the past several decades, such as civilian patrol missions aboard UAVs or shipboard systems, and industrial inspection tasks like factory defect scanning. Nevertheless, moving infrared small target detection still faces considerable challenges: existing models suffer from insufficient spatio-temporal semantic correlation and are not lightweight-friendly, while algorithms that perform reliably across diverse scenarios are in great demand for real-world applications. To address these issues, we propose FeedbackSTS-Det, a sparse-frames-based spatio-temporal semantic feedback network. A closed-loop spatio-temporal semantic feedback strategy with paired forward and backward refinement modules that work cooperatively across the encoder and decoder is adopted to enhance information exchange between consecutive frames, effectively improving detection accuracy and reducing false alarms. Moreover, we introduce an embedded sparse semantic module (SSM), which operates by strategically grouping frames by interval, propagating semantics within each group, and reassembling the sequence to efficiently capture long-range temporal dependencies with low computational overhead. Extensive experiments on many widely adopted multi-frame infrared small target datasets demonstrate the consistent effectiveness of our proposed network across diverse scenes.