Fusing Gradient, Intensity Accumulation, and Region Contrast for Robust Infrared Dim-Small Target Detection

Existing infrared small target detection methods often fail due to limited exploitation of spatiotemporal information, leading to missed detections and false alarms. To address these limitations, we propose a novel framework called Spatial–Temporal Fusion Detection (STFD), which synergistically integrates three original components: gradient-enhanced spatial contrast, adaptive temporal intensity accumulation, and temporal regional contrast. In the temporal domain, we introduce Temporal Regional Contrast (TRC), the first method to quantify target-background dissimilarity through adaptive region-based temporal profiling, overcoming the rigidity of conventional motion-based detection. Concurrently, Regional Intensity Accumulation (RIA) uniquely accumulates weak target signatures across frames while suppressing transient noise, addressing the critical gap in detecting sub-SNR-threshold targets that existing temporal filters fail to resolve. For spatial analysis, we propose the Gradient-Enhanced Local Contrast Measure (GELCM), which innovatively incorporates gradient direction and magnitude coherence into contrast computation, significantly reducing edge-induced false alarms compared with traditional local contrast methods. The proposed TRC, RIA, and GELCM modules complement each other, enabling robust detection through their synergistic interactions. Specifically, our method achieves significant improvements in key metrics: SCRG increases by up to 36.59, BSF improves by up to 9.48, and AUC rises by up to 0.027, reaching over 0.99, compared with the best existing methods, indicating a substantial enhancement in detection effectiveness.