Multi-Scale pattern-Aware task-Gating network for aerial small object detection

With the advancement of high-precision remote sensing equipment and precision measurement technology, object detection based on remote sensing images (RSIs) has been widely used in military and civilian fields. Different from traditional general-purpose environments, remote sensing presents unique challenges that significantly complicate the detection process. Specifically: (1) RSIs cover extensive monitoring areas, resulting in complex and textured backgrounds; and (2) objects often exhibit cluttered distributions, small sizes, and considerable scale variations across categories. To effectively address these challenges, we propose a Multi-Scale Pattern-Aware Task-Gating Network (MPTNet) for remote sensing object detection. First, we design a Multi-Scale Pattern-Aware Network (MPNet) backbone that employs a small and large kernel convolutional complementary strategy to capture both large-scale and small-scale spatial patterns, yielding more comprehensive semantic features. Next, we introduce a Multi-Head Cross-Space Encoder (MCE) that improves semantic fusion and spatial representation across hierarchical levels. By combining a multi-head mechanism with directional one-dimensional strip convolutions, MCE enhances spatial sensitivity at the pixel level, thus improving object localization in densely textured scenes. To harmonize cross-task synergy, we propose a Dynamic Task-Gating (DTG) head that adaptively recalibrates spatial feature representations between classification and localization branches. Extensive experimental validations on three publicly available datasets, including VisDrone, DIOR, and COCO-mini, demonstrate that our method achieves excellent performance, obtaining AP₅₀ scores of 43.3%, 80.6%, and 49.5%, respectively.