STGBD-Net: Spatio-temporal Gradient Basis Decomposition Network for Infrared Small Target Detection
Researchers have developed a novel framework for infrared small target detection (IRSTD) called STGBD-Net, which utilizes Basis Decomposition Theory to improve feature fusion. This approach reformulates the process into an adaptive decomposition-and-reconstruction paradigm, employing Gradient Decomposition Modules (GDMs) to treat normalized gradient features as basis vectors. The resulting networks, including spatial and spatio-temporal variants, demonstrate state-of-the-art performance on multiple benchmarks with enhanced accuracy and computational efficiency. AI
IMPACT Introduces a novel approach to feature fusion for improved accuracy and efficiency in infrared small target detection.